A song of ICC and Fire - A game of drones Assessing the effects of drone strikes - evidence from Pakistan

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A song of ICC and Fire - A game of drones

Assessing the effects of drone strikes - evidence from Pakistan

Torjus Havro Bjørnstad

Masteroppgåve i statsvitskap UNIVERSITETET I OSLO

Haust 31. oktober 2016

Ord: 23063

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Copyright Torjus Havro Bjørnstad

2016

A song of ICC and fire – A game of drones

Torjus Havro Bjørnstad

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Abstract

The use of militarised drones in conflict areas has increased exponentially since the first drone strike was recorded in Yemen in 2001. Now it is employed in conflict areas spanning from Syria and Irak, to Somalia. In this thesis I investigate the effects of drone strikes on militant violence, and attempt to improve on the understanding of its repercussions by focusing on the on-going drone campaign in the Federally Administered Tribal Areas

(FATA) situated in northwestern part of Pakistan (2004-). I argue that this campaign provide an opportunity to properly isolate the effects of drone strikes. Relying on a quasi-random identification strategy and the potential outcomes model for causal analysis, I argue that drone strikes have significantly increased the lethality of militant violence. However, contrary to earlier research, I argue that the indirect effect of drone strikes through its reported civilian casualties does not stand for much of the effect.

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Acknowledgements

Eg vil gjerne fyrst og fremst takke Scott Gates for god rettleiing og oppfølging.

Eg takkar og venner, familie og kjærast for all støtte og oppmuntring.

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List of tables

Figure 1: Causal structure single shot framework ... 22

Figure 2: Dynamic treatment regime ... 34

Table 1: Summary statistics confounding variables ... 49

Table 2: Summary statistics treatment, mediator and dependent variable ... 51

Figure 4: Temporal trend in violence ... 52

Table 3: ATE of drone strikes on militant violence in the FATA ... 54

Table 4: ATE of drone strikes on militant violence in Peshawar agency ... 55

Table 5: Civilians killed and wounded in Peshawar ... 56

Figure 5: Weekly weights (top FATA, bottom Peshawar) ... 57

Figure 6: Covariate balance ... 59

Table 6: ATE of drone strikes on militant violence in the FATA 2004-2011 ... 60

Table 7: ATE of drone strikes on militant violence in Peshawar agency 2004-2011 ... 61

Figure 7: Sensitivity plot ... 63

Table 8: Randomised indirect effects of drone strikes through number of reported civilian casualties in the FATA ... 64

Table A1: Peace agreements and military offensives ... 81

Table A2: Moran I for models for FATA and Peshawar ... 82

Figure B1: Difference in militant attacks, civilian casualties and wounded ... 83

Figure B2: Weekly weights civilian casualties ... 84

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1 Introduction

Are drone strikes an effective tool against militant violence? November 3. 2002 an unmanned aerial vehicle (UAV), more commonly known as a drone, fired guided missiles on the alleged al-Qaida leader Qa’id Salim Sinan al Harithi and five other alleged al-Qaida militants, as they were driving along a Yemeni road. This strike was the first known and recorded U.S. targeted assassination using a drone. Since then, the use of militarised drones has become a frequent military strategic mean in the U.S. led war on terror, and have increasingly been used in diverse conflict zones such as Syria, Iraq, Somalia, Afghanistan and Pakistan.

Despite drone strikes’ increasing popularity, the few studies that have attempted to investigate their effects have shown contradictory results. On the one side there is the argument that drone strikes have significantly reduced terrorist attacks and violence by being able to effectively target and kill terrorist leaders (Johnston and Sarbahi, 2016).

On the other, it has been shown that such strikes in fact at best have no effect on terrorist organisations’ operations (Jaeger and Siddique, 2011; Smith and Walsh, 2013) and at worst are counterproductive (Lyall, 2015).

In this thesis I attempt to improve on the understanding of drone strikes and its effects by focusing on the on-going drone campaign in the Federally Administered Tribal Areas (FATA) situated in northwestern part of Pakistan on the border of Afghanistan. I argue that this campaign provide an opportunity to properly isolate the effects of drone strikes. First because the province show homogenous characteristics and a common history of little state interference, that make the occurrence of drone strikes uncorrelated with key variables that often are argued to be drivers of militant violence. And second by implementing a dynamic method that allows me to estimate causal effects of drone strikes consistently over time. I also base my analysis on in- depth data compiled by The Bureau of Investigative Journalism (Ross and Serle, 2016).

This data includes accounts of reported drone strikes and its civilian casualties, which facilitates testing both the effects of drone strikes and the mechanisms that form militant responses.

This thesis proceeds as follows. The first section clarifies the type of violence drone strikes represent. Previous studies on the effects of drone strikes have either explicitly defined drone strikes as a selective violent mean, (Johnston & Sarbahi, 2016)

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or implicitly as indiscriminate (Jaeger & Siddique, 2011; Lyall, 2015). I then proceed in the second section to review the literature on the effectiveness of airpower. The third section presents the theoretical framework where geographical control and civilian support is emphasised. A fourth section describes the research design, including data and variables, and outlines the potential outcomes framework in the context of drone strikes and civilian casualties. I also introduce a newly defined approach from

epidemiology studies, assessing treatment and indirect effects in longitudinal settings, to the context of political science. At last follows a sixth and seventh section where the first of these evaluates the effects of drone strikes and its civilian casualties and the other encompass a discussion of the results and its policy implications. Finally, I conclude.

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2 Background and Motivation

2.1. Selective or random targeting?

The main argument backing the increased use of drone strikes in conflict areas is that the strategy is selective and precise; it kills it’s intended targets and there are few civilian casualties (e.g. Johnston and Sarbahi, 2016; Walsh, 2013; Young, 2013). Drone strikes have therefore been said to be a mean of a more personalised targeting of

individuals representing a threat.¹ In this way it is argued to be overall more effective in battling extremist violence than a violent act that for example involve bombing an entire village based on the presence of a relative few alleged militants. Following this line of argumentation, drone strikes can be seen as a mean of what has been categorised as selective violence; people are targeted on the basis of their own actions and through the assertion of “guilt” (Kalyvas, 2006).

The opposition of the drone program, however, argues that precision is not only a function of one’s the ability to enter a rough area and “surgically” eliminate one or more individuals, it depends first and foremost on whether one kills the “right”

individual(s) (Moyar, 2015). It is emphasised that one needs sufficient information and intelligence to first be able to assure individual guilt, but also, more importantly, that one targets and kills the intended person. Logically, it therefore follows that if a U.S.

drone strike then kills an innocent, or several innocent people, it may no longer be seen as selective. To illustrate, suppose that a drone fires missiles on a suspected Taliban training camp and reportedly kills 80 people, of whom one is a suspected militant and the rest civilian children.² The children will not be targeted based on individual guilt, but by guilt by association. This type of violence is what Kalyvas (2006) defines as indiscriminate violence. It aims to deter people from cooperating with the opponent actor by collectively penalise collaborators and their affiliates.

Since mid 2004, reportedly the beginning of the drone campaign in the tribal areas of Pakistan, till end 2014, between 421 and 957 civilians have been reported

1 Hence its accompanied label “targeted killing”.

2 The strike took actual place October 30^th 2006 and targeted a local madrassa, an Islamic

2 The strike took actual place October 30^th 2006 and targeted a local madrassa, an Islamic religious school, in the Bajaur agency. Such schools have been systematically targeted as many are seen as radical/ fundamentalist and practically militant training camps. It was the sixth recorded U.S. drone strike in the FATA (Serle, 2016).

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killed, compared to 294 alleged militants.³ These numbers, however, should be

considered conservative, as the drone programme remains highly secretive and the U.S.

government rarely publicly acknowledges one of their strikes (Scahill et al., 2015).

Much of the numbers are also based on identified casualties, and the majority of the killed remain unnamed.⁴ According to a report by Reprieve (2014), in the time period November 2002 till August 2014 twenty-four Pakistani and seventeen Yemeni militants were also reportedly killed and targeted multiple times. On an average, each of them were reported killed over three times, with Baitullah Mehsud having reportedly “died”

the most with seven (ibid., pp. 6–7). Two other militant leaders have also been reported killed six times, by which one is believed to still be alive today. In the same drone strikes, an overall 1,147 people were reported killed by which 142 in Pakistan alone were reported to be children.

At the same time, both the CIA in Pakistan and the JSOC in Yemen, Afghanistan and Somalia have also made use of so-called “signature strikes” (e.g.

Ackerman, 2016; Cavallaro, Sonnenberg, and Knuckney, 2012; McNeal, 2014).

Whereas personality strikes require the drone operator to develop a high level of assurance about the target’s identity, based on multiple sources as “imagery, cell phone intercepts and informants on the ground” (Miller, 2012), operators can also “initiate a signature strike after observing certain patterns of behaviour” (Holewinski, 2012). Since the end of President Bush’ tenure, the majority of the drone strikes have been of this type. According to the leaked ISR documents (Scahill et al., 2015), for the CIA to actually carry out a drone strike a “high level of assurance is required”. Positive identification of the target must be established with “near certainty”, and drones may only strike in confirmed low “Collateral Damage Environments” (ibid.). Nevertheless, these criteria have been highly subjected to scrutiny as it has been reported that they in fact are as vague as “military aged males” in areas where terrorists are known to operate (Becker and Shane, 2012). A drone strike that kills a military aged male is then counted as having assassinated an alleged militant. Overall, this runs counter to the argument that drone strikes are personalised and selective.

3 The numbers are compiled by The Bureau of Investigative Journalism (Ross and Serle, 2016).

4 The total number of individuals reported killed ranges between 2418 and 3883 (Ross and Serle, 2016). The people unaccounted for in the numbers are therefore many.

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In light of the apparent lack of a “near certainty” that a target is actually present at the strike location, the use of vaguely defined signature strikes, and the reported number of civilians killed, at a minimum questions the preciseness and selectivity of drone strikes. This seems also to be reflected in the view of the population residing in the tribal region, where over three quarters oppose the U.S. drone campaign and only 16% think the strikes accurately target alleged militants (Ballen, Bergen, and Doherty, 2013, p. 250). I therefore argue that even though one may have the practical capabilities in hand, the insufficient amount of information, the cost and institutional constraints to punish militants individually makes the current use of drone strikes an indiscriminate mean of violence in the conflict against the militant organisations in the FATA. Indeed, rather than being seen as a selective punishment, drone strikes are effectively a more geographically concentrated airstrike.

Can an indiscriminate violent mean reduce militant violence?

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3 The debate

3.1. The effectiveness of drone strikes

Although indiscriminate, several academic works on coercive airpower have argued that the strategy can effectively reduce militant violence both in incidences and lethality. In earlier literature on the effects of drone strikes two mechanisms has been highlighted:

drone strikes can disrupt the militant organisation’s ability to carry out terrorist activities, and degrade the militant organisation by removing higher ranked cadres (Johnston and Sarbahi, 2016). Following these two mechanisms, it is argued that the use of drone strikes is selective and precise; it kills its intended targets, there are fewer civilian casualties, and it is overall more effective in battling extremist violence.

The first mechanism entails thus that drone strikes coercively disrupt and weaken the militants’ ability to effectively operate. By destroying strategic outposts, impeding supply lines and the crucial provision of goods, and eliminating insurgents at a faster pace than the replacement rate, drone strikes limit the insurgent or terrorist group’s capacity to cohesively remain in control of an area. In addition to Johnston and Sarbahi (2016), several studies focusing on airpower’s disruptive effect argues in its favour. Recently Lyall (2009) convincingly demonstrated that the indiscriminate barrage pattern employed by the Russian military in Chechnya from 2000-2005, in fact approximated a lottery assignment mechanism. By following a standardized barrage pattern known as “harassment and interdiction”, consisting of shelling at “random intervals and of varying duration on random days without evidence of enemy movement”, and with many “drunk (or high) soldiers often participating in combat operations”, he finds evidence that shelled villages experienced a substantial reduction in insurgent attacks relative to non-shelled ones (Lyall, 2009, pp. 343–347).

While Lyall (2009) looks at airpower’s isolated effects, in their study of the NATO bombing in Kosovo in 1998-1999, Byman and Waxman (2000) puts emphasis on airstrikes’ effects through and jointly with other strategic actions on the subsequent regime change. The threat of a ground intervention, Russia’s decision not to support the sitting regime and the resurgence of Kosovo Liberation Army (KLA) were also key factors for Milosevic’ capitulation (ibid.). However, they highlight that the bombing campaign gave the threat of a ground intervention legitimacy, strengthened the KLA’s

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cause and partly eroded Russian support. In all, this consequently led to the demise of the sitting administration.

Drone strikes may also have a deterrent disruptive effect, in that it effectively dissuades insurgents from executing attacks in fear of future drone strikes. In larger airpower campaigns, a frequent strategy to signal one’s military presence is to simulate bombing runs where no weapons are released. Such “shows of force” have for example been used in great scale in the U.S. led military intervention in Afghanistan (Lyall, 2015). However, unlike bigger bomber airplanes, drones have the capacity to remain in flight for as long as 40 hours gathering surveillance⁵, and if a target is identified with

“near certainty” the drone operators may decide to strike (Scahill et al., 2015). The population residing in the FATA have also reported that the “buzzing” sound that Predator and Reaper drones make is almost at a constant, blending in with everyday activity (Shah, 2013). So where in previous conflict zones the insurgent’s security risk has mostly been associated with the sights and sounds of shows of force, and violent attacks and clashes with the opposing camp, the technological advancements in

remotely operated vehicles capable of striking at every moment, now make it a 24-hour endeavour. With the use of signature strikes, where even patterns of movement deemed suspicious can get you killed, the risk of being an insurgent is dramatically increased (De Luce and McLeary, 2016).

The second mechanism through which drone strikes can decrease militant violence is by degradation. This mechanism suggests that drone strikes’ ability to eliminate higher-ranking terrorist leaders or so-called “high-value individuals” (HVI’s) degrades the organisations ability to continue their activities. Byman (2006) calls this mechanism decapitation; one removes the organisational head keeping the rest of the organisational body unable to continue functioning. Killing individuals with important skillsets, resources or local connections degrades the militant groups command structure and decapitates the organisation’s ability to plan and carry out operations (Byman, 2006; Johnston, 2012).

In social network analysis it is emphasised that social ties between actors are the primary means by which to understand the functioning of an organisation (Thompson,

5 Citing a press release by General Atomics Aeronautical Systems, a leading manufacturer of Remotely Piloted Aircrafts http://www.ga.com/predator-xp-sets-new-company-record-for-long- endurance-flight, last visited 22.10.2016.

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2007; Wasserman and Faust, 1994). Individuals with the most social ties are crucial to organisational planning, and their removal may weaken an organisation. If organisations have networks susceptible to the removal of leading figures, decapitation will be

effective (Jordan, 2009). It is also a common consensus that hierarchically ordered organisations are more vulnerable to targeting of leadership than more decentralised structured groups (Byman, 2006). In such organisations leadership may be clearly identified and is more visible, increasing the probability of being assassinated. These individuals also enjoy greater social ties with other members in the group and are the one’s directly responsible for planning action. Effective military is also often

hierarchically structured (Cooley, 2005). Killing these individuals can thus lead to a degradation of command and a less coherent insurgent military, resulting in a decrease and less lethal militant attacks.

In their study investigating the degrading and decapitation effect of targeted action, Langdon, Sarapu, and Wells (2004) finds evidence that movements that have had their leaders killed appear to be more likely to fail. This study have been criticised for basing its findings on a quite small sample consisting of only thirty-five cases of leadership removal, spanning from 1750-2004, thus trying to explain variation of the effectiveness of degradation across several types of organisations with few common traits over a time-period of 250 years. However, the main conclusion of Langdon et al.’s study seem corroborated by Price (2012). By using a dataset consisting of 207 terrorist organisations in sixty-five countries spanning from 1970-2008, he finds that groups having experienced leadership removal, especially in their infancy, have a significantly higher mortality rate. This seems also to be the case for any leadership turnover, not just by having leaders killed by a foe (ibid., pp. 43–44).

3.2. The case against drone strikes

On the other hand, much academic work have challenged the assertions of airpower’s violence-reducing effect, arguing that its violence at best have no effect, and at worst is counterproductive (i.e. Corum and Johson, 2003; Kalyvas, 2006; Lyall, 2015).

First, it has been argued that the disruptive and degrading effects of airpower are grossly overstated. It has for example been shown that as an organisation grows in size and age it is much more likely to withstand the removal of both lower level operatives and leadership (Carley, 1991; Jordan, 2009). If one militant is killed, other capable

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members can step up and take his or her place in the hierarchy. The removal of leadership may also even strengthen the group’s resiliency and resolve, by increasing the resentment towards the incumbent and bettering group morale. This may result in more retaliatory attacks and increased public sympathy.

Jordan (2009) finds evidence that this is especially the case for organisations founded on religious beliefs. Compared to groups founded on ideology, religious groups tend to have a more decentralised leadership structure, and are often bigger in both size and age (Sageman, 2008). This decentralisation, often described in many different shapes as for example chain, cell or having a star structure, makes leaders and leader hubs more difficult to identify compared to in hierarchical ones (Carley, 1991). As a consequence this makes them harder to target. It also makes the organisation less dependent on specific individuals for their survival: As power is more spread

organisational adaptation is more likely enabling the emergence of new leaders (ibid., p.

86). The age and size of the organisation indicates the presence of a greater support base and a large pool of available members ready to take the place of higher ranked

operatives if they are removed. Unintended consequences of disruption and degradation then include the creation of a martyrdom effect, surge in recruitment, and a

strengthening of the group’s power and capability to carry out attacks (Jordan, 2009).

Indeed, Jordan (2014) argues that this is the case in the U.S. government’s pursuit of al-Qaida leadership. In her examination, she argues that terrorist organisations that are organised in bureaucratic forms and has substantial levels of communal support are more likely to survive attacks on their leadership than those that do not. The

targeting of al-Qaida has not and is not likely to result in organisational decline or long- term degradation. Although it has been weakened since the 2001 invasion of

Afghanistan and subsequent efforts to kill key-leaders, its bureaucratic structure and support in the local population have allowed it to withstand these frequent attacks.

Second, it has also been argued that airstrikes can remove the collective action problem facing insurgent groups; even though revolting and ultimately removing the current regime may be in the collective’s best interest, rational individuals will rather reap the nonexcludable benefits by “free riding” on possible insurgent victory than risking one’s life sowing them (Ginkel & Smith, 1999; Lichbach, 1995; Olson, 1965;

Tullock, 1971). This version of prisoner’s dilemma prevents mass recruitment to both violent and non-violent revolts.

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One mechanism often argued to eliminate this collective action problem is violence ability to create grievances. It has long been noted that state oppression can create sufferings in the civilian population that form opportunities to stage a rebellion and civil war (i.e. Gurr, 1970, 1993; Hegre, Ellingsen, Gates, and Gleditsch, 2001;

Tilly, 1978). Repressing airpower is by no means an exception. Empirically, civilians are killed regardless of the supposed precision of drone strikes. With the existence of a rival authority, desire of revenge and feelings of injustice can lead the population to side and join the rivalling faction facing sweeping airpower (Ladbury, 2009; Petersen, 2001). This may especially be the case if the drone strike campaign accentuates sustained systematic inequalities already in place in society (Cederman, Weidmann, &

Gleditsch, 2011; Stewart, 2008). Grievances thus increase militant recruitment and support, further forging the revolt.

A recent report by the Mercy Corps (2015), investigating the recruitment of youth to rebel organisations in Afghanistan, Sudan and Colombia, finds evidence suggesting that the main motivations to enlist in the Afghan Taliban were feelings of anger and of being wronged by western powers; “I didn’t join the Taliban because I was poor, I joined because I was angry. Because they (the west) wronged us.”(ibid., p. 22).

The former insurgent cited here, continues to explain how the bombing of a local madrassa was the decisive action that made him enlist in the Taliban. According to recent numbers from Internal Displacement Monitoring Centre (IDMC) more than 1,5 million people were in 2015 displaced in the FATA province as a consequence of the current insurgency and counterinsurgent operations.6 Being forced to leave one’s home, friends and perhaps family may also be a reasonable source of grievances and a want for revenge.

Rational militant organisation can thus capitalise on these feelings of injustice by shaping their image to encompass the newfound surge of desires of vengeance and provide an opportunity to get back at the aggressor (Wood, 2003). Oppressive action creates such a public demand for a revolt by which the militant organisations can exploit.

A second mechanism recently described in the academic field, is that

indiscriminate violence can encourage insurgents to take costly actions that build and

6 http://www.internal-displacement.org/south-and-south-east-asia/pakistan/figures-analysis. Last visited 05.10.2016.

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maintain their reputation for resolve both in the eyes of the counterinsurgent, but more importantly the civilian population (Lyall, 2015). The wake of a drone strike represents an opportunity to signal that the militant organisation still retains the organisational capacity to harm opponents. By creating a reputation for resolve and resiliency facing attempts to coerce and break the insurgency, one can shape the possibility and contours of a potential political settlement (ibid., p. 5). As states may gain from investing in and building a reputation for honesty to gain favourable positions in international

negotiations (Sartori, 2005; Tomz, 2007), and a reputation for toughness to deter separatist movements (Walter, 2009), militant organisations may also get a bigger chair at the negotiating table by responding to state transgressions with increased violence, demonstrating one’s ability to absorb punishment and inflict harm.

Lyall (2015) investigates this mechanism based on newly declassified United States Air Force (USAF) data on both larger air operations and drone strikes in

Afghanistan. He finds strong support for the reputational mechanism and concludes that airstrikes significantly have increased militant violence. Drone strikes also display (barely) significant positive effect on subsequent militant violence. However, given the lack of data on drones’ movements in the area, he cannot isolate the reputational mechanism for such strikes. As noted earlier, drones can remain in flight for long periods of time and there are few practical arrangements that need to be in place before a drone can take off from an airfield. The positive relation Lyall finds between drone strikes and militant violence can thus measure other mechanisms.

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4 Theoretical framework: control and security

A factor that often is highlighted in explaining foothold of militant organisations is the relative scarce governmental control and effective institutions in the areas in which they emerge. Recently, Cockburn (2015) argues for example that one of the main factors that led to the emergence of Islamic State of Iraq and Syria (ISIS) in Iraq, was that the Shia dominated government did not enjoy legitimacy in the Iraqi Sunni populated provinces.

This was also accentuated by the fact that the security forces in these areas were highly corrupt, and perceived as illegitimate (ibid., pp. 14–16). On the day that Mosul fell to the control of ISIS, June 10 2014, only one in three soldiers (60,000 out of almost 190,000) stationed there was actually present, with the rest paying up to half their salaries to their commanding officers to stay on permanent leave (ibid. p. 11). By taking Mosul, ISIS represented thus a real contender for the control of the larger Sunni

populated areas, and could, at least initially, protect the civilians from state oppression.⁷ These insights are consistent with arguments that highlight state capacity, and the associated observation that insurgencies are likely to develop and maintain civilian support in areas where state control has decreased or collapsed (Fearon & Laitin, 2003;

Kalyvas, 2006; Skocpol, 1979). Control over the civilian population and their desire for protection and security is an aspect overlooked in the theoretical accounts reviewed above. No matter who is in full control of the area, the civilians will support the side that can guarantee their security. This support may manifest itself as increased militant recruitment, but also as a tacit acceptance. In this section I develop a theoretical foundation for the relation between drone strikes and militant violence.

4.1. Control and collaboration

Drawing on Kitson (1971) and Kalyvas' (2006) “logic of violence” in civil war, control over geographical areas and support from the civilian population residing there is a defining feature for the dynamic interaction between incumbent and challenger. Control will first and foremost give rise to allegiance and collaboration with the civilian

population, thus solving the collective action problems both sides of the conflict may

7 It has been reported by several sources that the de facto rule of Mosul has been to run a violent and repressive campaign against the citizens in the areas they control (e.g. Amnesty

International, 2016; Lang and Gulati, 2015). Without jumping to conclusions, this may be one factor in explaining the recent attempts to retake the city.

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encounter. By credibly providing benefits and protection from violent acts of the other part, control also lowers the cost of collaboration with the established authority and such deters opposition (Gross, 1979).

Control over longer time periods can also spawn robust informational channels that facilitates direct monitoring and population control, again making denunciation and defection costly and cooperation beneficial. In this way it can also signal credibility, both in the short-term by sanctioning and long-term by promising benefits based on expectations about the outcome of the war (Kalyvas and Kocher, 2007). Kalyvas (2006, p. 133) argues for example that effective authority and policing is the reason why

“incumbents tend to control cities, even when these cities happen to be social, religious, or ethnic strongholds of their opponents, whereas the insurgents’ strongholds tend to be in inaccessible rural areas, even when the rural populations are inimical to them.”

Accordingly, control trumps pre-war political and social support (Kocher, 2004).

Thus, all in all, the higher level of control one actor credibly projects over a geographical area, the higher the collaboration with this actor will be and the lower the rate of defection. In setting of civil conflict, how then does drone strikes affect the distribution of control?

Kalyvas (2006) posits that as an actor gains more control over a territory, the less likely is it that this actor will resort to violence at all in this territory. If the actor also achieves full dominant control over the area, the other actor will neither, as defections are unlikely and possible denunciations most likely are false. However, in areas where one actor only enjoys low levels of control, and the other is in hegemonic but not in complete control, the more likely is it that the violence exercised is

indiscriminate.

4.2. Protection

Following the reasoning of control and civilian support, drone strikes, as an

indiscriminate violent mean, should be ineffective. As noted earlier, indiscriminate violence implies collective targeting, and the concept of individual guilt is consequently replaced by the concept of guilt by association. With the apparent lack of connection between a civilian’s actions and infliction of punishment, the threat of violence becomes unpredictable. The unpredictability makes the population fear for their life regardless of one’s behaviour. The violent acts will therefore create, at least initially, “a paralyzing,

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turbulent and irrational fear”, because compliance guarantees no security for the civilians and innocence is irrelevant (Kalyvas, 2006, p. 143). Civilians believing they have nothing to fear may also take less precaution and consequently be hurt or killed.

As complying with the counterinsurgent is as unsafe as not complying, the militants are no more threatened than the civilians. If one also risk getting killed if not joining one of the factions, one will choose the one that provides the best protection against the other (Kalyvas and Kocher, 2007; Kocher, Pepinsky, and Kalyvas, 2011). Drone strikes can therefore even be counterproductive, and lead the civilian population to seek the rivalling forces for protection.

In such a scenario, participation entails no collective action problem, but non- participation does. Collaborating with the rival militant organisation may therefore increase one’s chance of survival. This is well illustrated in an interview with a 24-year- old Iraqi man after the U.S. led invasion in 2003: “When the Americans fire back, they don’t hit the people who are attacking them, only the civilians. This is why Iraqis hate the Americans so much. This is why we love the mujahedeen.” (cited in Condra and Shapiro 2012, p. 167). Even though drone strikes may be more precise than other forms of airstrikes, they are still an indiscriminate violent mean undermining the civilians’

security. Increased collaboration increases militants’ ability to carry out with their activities, reaching further into the areas where the government is in dominant control.

Having a broad network of information facilitates and such increases militant attacks.

If the population also specifically blame the incumbent for the indiscriminate violent campaign conducted by a third party,

4.3. Divide

According to the logic of violence put forward above, the competition over the distribution of control and civilian support is a two-player game. This puts the current use of drone strikes in a special position. In the conflict areas where drone strikes have reportedly been used, neither the sitting incumbent, nor the rival actors have been the ones predominantly employing it. Third parties, as the U.S. government,⁸ have primarily been the actor putting it into practice, intervening under the justification of

8 It has recently been reported that Russia employs drones in Syria (Crawford, 2015), as well as Turkey, Syria, Iran, Iraq and Britain (Kajjo and Jedinia, 2016). Syria serves thus as a special case where a myriad of actors have used militarised drones.

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preventive war or coming in aide of its allies. This crucially allows the third party actor to disregard the issues related to using indiscriminate violence in areas where one of the rivalling actors has full dominant control, as it has little political repercussions for that specific actor. The civilian population in these areas may be aggrieved, but they have little power to change the campaign.

The mechanism divide posits thus that drone strikes can drive a wedge between and alienate the population from the insurgents even in areas where the militants are in full control. By no longer being able to properly guarantee the safety and protection from outside violence, allies and the civilian population may withhold aid and

information, and even switch side and denounce the militants to the incumbent or U.S.

intelligence officers (Byman and Waxman, 2002; Lyall, 2009; Pape, 1996). Drone strikes thus undermine the power and control the insurgents exerts over an area and restricts their ability to continue to fight. Filkins (2001, p. A1) cites for example a Taliban defector after the U.S. led coalition in Afghanistan started cooperating with the opposition group Northern Alliance: “I joined the Taliban because they were stronger. I am joining the Northern Alliance because they are stronger now.”

The dividing effects may also especially be the case if the local population blame the militants for the repressive strategies by the other parties. The militants can in this way be forced to curb, if not entirely abandon, its tactics and strategy to avoid provoking further violent countermeasures (Lyall, 2009).

The logic behind this mechanism is thus that if one cannot properly identify the militants then the violent mean target people based on their association with them. The assumption is then, that these people will either force the militants to alter their

behaviour or the militants to change their course of action. Under World War two (WW2) for example, when Norway was under German occupation, the Norwegian resistance did in fact acknowledge this and halted their operations facing indiscriminate reprisals put forward by the occupation forces (Riste and Nøkleby, 1973, p. 68). In a similar way, drone strikes may spread the responsibility of the militancy, and make the targeted population collaborate because it fears their sanctions more than the ones put forward by the rebels.

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4.4. Hypotheses

How does these theoretical assumptions translate into empirical indicators? First of all, capturing the true extent of each of the mechanisms described above is a near

impossible task, with which this thesis will not be able to solve. However, as I have argued above, the dynamics of violence in civil conflict is dependent on geographical control and civilian support. I therefore mainly concentrate on drone strikes’ effect on militant violence through its reported number of civilian casualties.

Second, as is recognised in most of the academic field, without access to in- depth qualitative and survey data, the dynamic relationship between insurgents, counterinsurgent(s) and civilians is impossible to properly assess. With access to observational data the goal in this thesis is therefore more moderate. Following the theoretical discussion above, this thesis attempts to test hypotheses on how drone strikes and its civilian casualties affect the direction and size of militant violence.

The overall theoretical framework of control and civilian support, leads to the following hypothesis:

H1 Drone strikes are ineffective does not increase militant violence.

This prediction, however, is observationally equivalent to both grievance-based accounts and reputational dynamics. Facing a similar issue, Lyall (2015) investigates simulated bombing runs, shows of force, as well as actual airstrikes to isolate the reputation account from the grievance explanation. As I have already mentioned, this is harder to do in the case of drone strikes, as drones can remain in the air for long periods of time. It has also been reported that in the FATA region of Pakistan, the buzzing sound that drones make, is a common trait in everyday life.

In this thesis, to separate between theoretical accounts I therefore include three additional tests. The reputational mechanism posits that the militant response after a drone strike should follow the logic of a “quick fuse” rather than a “slow burn”. It should also be geographically centred in the close surroundings of where the drone strike occurred (Lyall, 2015, p. 9). Control and civilian support, should on the other hand build up over time, where its effects first manifest themselves several weeks or even months after the strike. The effects should also not occur in areas where the militant organisations have full effective authority, but rather in areas where the

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incumbent is the hegemon. In the subsequent analysis I therefore include several measures of militant violence over time and in other geographical areas than where the militants typically have been in control.

To test the two mechanisms divide and protection I also include measures for the reported civilian casualties of drone strikes. I here decompose the assumed relation in hypothesis 1 into two additional hypotheses. Even though drone strikes overall are considered to be ineffective:

H2 According to the protection mechanism, drone strikes that kill one or more civilians will increase militant violence.

H3 According to the dividing mechanism, drone strikes that kill one or more civilians will not increase militant violence.

To distinguish the mechanism divide from the grievance-based explanation is

straightforward. Driving a wedge between the population and the militants will lead to a decrease in militant support and hence violence will not be a fruitful path. An aggrieved population will increase militant support, which consequently will increase militant use of violence in areas of incumbent control in the long run.

For the protection mechanisms, it is harder to empirically distinguish its effects from the grievance-based explanation. Both predict an increase in militant violence, and would follow similar empirical paths. Grievances may accumulate over time and should also follow the “slow burn” logic. However, given that drone strikes mainly are and have been conducted by a third party in conflicts, the feelings of injustice and revenge should be directed at the third party and not necessarily against other conflicting camps.

In this way, the grievance-based mechanism should therefore not spark recruitment only to the militant organisations. It should then spark support to the actor that is best

equipped to respond to such third party infringements and guarantee the safety of the civilians.

One exception is if the third party is intrinsically linked to the incumbent. Then the civilian population may ascribe the violent act to the incumbent, thus unilaterally giving its support to the militant groups. One must therefore argue for this proposition empirically.

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5 Research design

To test these competing accounts, I use the on-going CIA drone campaign in the FATA region as an identification strategy to isolate the causal effect of drone strikes on

militant violence. I focus on this area because it has some important properties

strengthening causal claims. First of all, its history of little to no state interference, the emergence of militant organisations and the continuously increasing violence, makes it what Eckstein (1975) would categorise as a “most likely” case for observing the link between attempts to challenge militant control and the subsequent militant violence. For example the first time the Pakistani military sat their feet in after the independence of Pakistan in 1947, was in 2001. Since then, the level of violence have dramatically increased, especially after 2004. Second, it is almost exclusively inhabited by Pashtun tribes (99,10%) and is covered in mostly mountainous and rugged terrain (Minority Rights Group International, 2009). All of these characteristics should make the drone strikes uncorrelated with key spatial and demographic variables oft argued to be drivers of militant attacks.

My empirical analysis is also helped with the quasi-randomness of the

assignment drone strikes. As detailed in an earlier section, the majority of drone strikes, both in Pakistan and elsewhere, are signature strikes, where patterns of movement are targeted if deemed similar enough to the movement of militants. The criteria for such strikes are very vague, which in effect makes assignment of the majority of drone strikes quasi-random. The fact that drones stay in flight for long time periods at a time, gathering intelligence and being able to strike at almost any time, also contributes to this quasi-random assignment of drone strikes and its casualties.

The CIA is also the main actor conducting strikes in the area. While JSOC operations are defined as armed forces, the information about their strikes is publically available. This is not the case for CIA drone strikes as they are defined as covert action (McNeal, 2014). It has also been reported that the CIA strikes have higher civilian death tolls than the ones conducted by JSOC (Scahill et al., 2015). As one of the criteria for conducting a drone strike is that the targeted area is a low “Collateral Damage

Environment”, this adds to the quasi-randomness affecting the assignment of drone strikes.

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However, the most important determinants of militant violence must still be accounted for; namely the dynamics that emerge from the interaction of U.S. and militant strategy. To account for this interaction, I adopt Inverse Probability of

Treatment Weighted (IPTW) estimators. This method is an extension of the propensity score weighting scheme to dynamic settings, and it essentially weights each observation of its probability of being struck by drone strikes. It thus creates a reweighted dataset where variance created by confounders is alleviated. In this way the effect of drone strikes can be properly isolated. Also, to thoroughly define the causal effects I try to identify, I rely on the counterfactual approach to causal inference.

In this section, I first provide a brief review of the origin of FATA province as an administrative unit and the emergence of militant organisations in the area. Then I define the causal effects of interest using the potential outcomes framework. Third, I proceed to describe the empirical strategy and the data and variables included in the analysis.

5.1. Little to no state interference

The FATA province is located in the northwestern part of Pakistan bordering the Durand line and Afghanistan to the west, the province Khyber Pakhtunkhwa to the north and east, and to the Balochistan province in the south. It comprises 27,200 square kilometres and consists of seven tribal agencies (districts) and six-frontier regions.

FATA’s population of approximately 3 million is predominantly Pashtun, a group of tribes that settled in the area more than 1,000 years ago (Lieven, 2011).

The origins of the tribal areas as a federal administrative unit, was to actually divide and weaken the Pashtun tribes residing here and in Afghanistan, and turning Afghanistan into a buffer zone between British and Russian empires (Zissis and Bajoria, 2007). The border to Afghanistan derives its name from the British diplomat Sir Henry Mortimer Durand that negotiated the territorial split with the Afghani government in 1893. Post 1893, the British established an indirect rule through the laws called the Frontier Crimes Regulations, which gave the Pashtun tribes freedom to govern their internal affairs according to their own tribal codes and customs (Abbas, 2010, p. 8). The colonial administration only held the authority in some “administered” areas and

adopted a patronage system, maliki, to subjugate the tribes. The maliki were, and still

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are, tribal elders set up to solve disputes and call decision-making assemblies called jirga (ibid.).

After the independence in 1973, the same system has more or less remained in place. Any law passed by the Pakistani Parliament have no effect in the tribal region and no Pakistani court have jurisdiction. Only the President of Pakistan has the constitutional power to issue and enforce new regulations (Pakistani Constitution art.

51, 59 and 247). Traditionally, the executive’s role has been limited to supervising development programs and policing. In practice, however, the Civil Secretariat FATA in cooperation with the Governor of the neighbour province Khyber Pakhtunkhwa administers the overall development, and, apart from the small presence of some Frontier Corps, the Pashtun tribes have enforced their own rules and regulations (Rubin and Siddique, 2006).

In view of its history and the fact that the state interference in the FATA has historically at most been at a minimum, a multitude of militant groups have settled in the area. Many of these groups have roots from the anti-Soviet mujahideen movement that operated in the late 1970s and 1980s (Gul, 2009). During the Afghan-Soviet conflict, FATA served in many ways as a safe haven and training facility for mujahideen militants outside of the conflict prone areas in Afghanistan. This was facilitated by the cross-border cultural heritage, with Pashtun tribes living on both sides of the Durand line. The aftermath of the Soviet-Afghan war thus left the tribal regions with an enormous infrastructure for war in terms of recruits, weapons, ideology and training centres⁹, which in effect laid the groundwork for the on-going militant campaign (H. Khan, 2012, p. 103).

The 2001 9/11 attacks have also had an enormous effect on the current climate in FATA. The subsequent U.S. invasion and occupation of Afghanistan created a rush of al-Qaida¹⁰ and Taliban militants from Afghanistan to cross into the FATA, as the area again was located outside of the locus of battle. Making use of old mujahideen ties and tribal networks, the foreign fighters settled down and joined armed local groups. As a consequence of the increased influx of fighters, the U.S. expanded their military strategy into the FATA, relying on missile strikes launched from drones (Nawaz and de

9 Allegedly, these facilities was developed by the Pakistani government and financed by, amongst others, the CIA (H. Khan, 2012, p. 103).

10 The senior leaders and the core group of al-Qaida sought refuge in the tribal areas already in late 2001 and 2002 (Rubin and Siddique, 2006).

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Borchgrave, 2009, pp. 9–10). The main groups operating in the area today are the Tehrik-i-Taliban Pakistan (TTP), al-Qaida and the Haqqani Network.

From 2004 until today the Pakistani military strategy in regards to the conflict in the FATA has been twofold. I has mainly consisted of peace agreements and, more recently, military action. Under intense pressure from the U.S., the Pakistani military entered in March 2004 the Kaloosha village just outside the largest town in the agency South Waziristan Wana, with the intention of clearing the local militant groups they claimed to be in the “few hundreds” (H. Khan, 2012, p. 104). The backdrop of the operation was that for the past several years, with the increasing influx of Afghan Taliban and al-Qaida forces, local militant groups had staged an uproar against the perceived corrupt malik system, and hence the Pakistani government’s control in the area (Abbas, 2010, p. 8). Under the operation, however, the local militants ambushed the troops and many soldiers were killed and taken as hostages. Due to the increasing damages both sides suffered and the lack of civilian support for the military’s

indiscriminate shelling of the village, the Pakistani government opted for a peace deal with the militants, later called the Shakai agreement. The agreement was signed April 24 but remained only in place for 50 days, as the military operation was re-launched June 11 (Cavallaro, Sonnenberg, and Knuckney, 2012). Similar agreements was reached with militants residing in North Waziristan in 2005, called the Miranshah peace accord, in Khyber in 2008, and in the Swat valley after the much mediatised 2008 military offensives.

The apparent motive of these agreements was to prevent the expansion of the conflict zone, and to steer clear of direct collisions with the militants, many of whom have previously had a good relation with the security forces (Abbas, 2010, p. 9).

However, as Tajik (2011, p. 14) notes, the fact that all of these peace deals was followed by military operations against the militants in the area is a testimony to their failure. The agreements also mainly focused on areas of concern to the military itself and the security personnel. The militants was in return offered a free hand in the area, and consequently given legitimacy as an equal party vis-à-vis the state (ibid.). The government also undermined its status by signing the pacts in the headquarters of the militancy, which by tribal tradition of Nanawatay meant that the government accepted the whole responsibility of the entire conflict (H. Khan, 2012; Tajik, 2011; Yusuf, 2014).

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Figure 1: Causal structure single shot framework

In light of the failures of the past peace agreements, the Pakistani military have after 2008 therefore relied more heavily on military solutions and a more maintained military presence.¹¹

5.2. The potential outcomes framework

As emphasised in the past sections, it is in this thesis of theoretical interest to

investigate both the total causal effect of drone strikes on militant violence, and how much of this effect is moderated or accentuated through the killing of civilians. I have argued that looking at these effects provides a better test of the proposed theory, than for example looking at the alleged militant death tally. To develop a sound basis to draw valid inferences of these effects, I rely on the potential outcomes model for causal inference. This framework was first outlined by Rubin (1974, 1978), but has firm roots in the experimental design literature by Neyman (1990[1923]) and Fisher (1935). The attraction of this framework is that it formalises the counterfactual model of causality and presents causality as a relation between observed outcomes and unobserved theoretical potential outcomes.

The design also allows me to easily incorporate heterogeneous effects and, importantly, separate the definition of a causal effect from its estimation (Imai, Keele, and Tingley, 2010). Whereas in more traditional linear structural equation methods the

11 A comprehensive list of peace agreements and military offensives are given in appendices A1.

X

M

A Y

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assumptions needed for interpreting causal effects are tightly associated with specific statistical models, e.g. so that generalising from linear to nonlinear models is

impossible, the potential outcomes model can on the contrary be used for the identification and estimation of causal effects without any reference to statistical

method and parametric restrictions by relying on theoretical counterfactual quantities. In this way it clarifies and bridge the distinction between statistical estimation and

inference through theory.

The core aspect of the model is fairly simple. Assume that in a population of interest each unit 𝑖 can potentially be exposed to two alternative states of a cause 𝐴_!. Each state is characterised by their exposure status and to which it potentially affects some outcome of interest generally noted 𝑌_!. Given the method’s roots in experimental research design, the causal states of a binary cause 𝐴_! are often labelled treatment, 𝐴_! = 1, and control, 𝐴_! =0. In the context of this thesis, the units of observation are the seven administrative agencies (districts) comprising the FATA region of Pakistan.

Agencies having been struck by drones between June 2004 and end December 2014 are denoted as treated, 𝐴_! = 1, and agencies having not been struck by drones in the same time period are denoted as control, 𝐴_! =0. 𝑌_!(𝑎) will then denote the potential level of militant violence that would result under the realised treatment status of drone strikes 𝑎.

Following this setup, I would then proceed to estimate the effect of a single action on an outcome analysed to be at a single point in time, namely the time period from June 2004 to end 2014. This is called a single-shot framework.

The key notion of this framework is that each unit in the population of interest has the potential to be exposed or not exposed to the action of a cause (Holland, 1986).

It therefore follows that for the administrative agency 𝑖 the causal effect of the drone program can be defined as 𝑌_!(1) – 𝑌_!(0), the difference in militant violence for the agency having been struck by drones relative to the same agency having not been struck by drone fired missiles. However, the quantities 𝑌_!(1) and 𝑌_!(0) are impossible to observe for the same agency; an administrative agency cannot both be hit by drone strikes and not be hit by drone strikes at the same time. It is therefore also impossible to observe the causal effect of 𝐴_! on 𝑌_!. So for the causal effect of having been struck by drones rather than not having been struck by drones, administrative agencies that did not experience a single drone strike will only have a theoretical counterfactual what-if level of militant violence under the treatment state “having been struck by drones”, or

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𝐴_! = 1. One of the causal states will hence only exist in theory. This is by Holland labelled as the fundamental problem of causal inference.

Total and natural direct and indirect effects

To overcome the fundamental problem of causal inference researchers often focus on the identification and estimation of an aggregated effect, also known as the average treatment effect (ATE) (Morgan and Winship, 2015, p. 46). This effect is defined as 𝐸(𝑌_!(1)−𝑌_!(0)), where E(.) denotes the expectation over units in the population of interest, which in this thesis are the seven administrative agencies in the FATA. If treatment is randomized, as it would under a controlled randomised experiment, then 𝐴_! would be statistically independent of potential outcomes. The following property is then achieved 𝑌_! 1 ,𝑌_! 0 ⫫𝐴_!. This is often referred to as the ignorability assumption;

that there are no systematic differences between those treated from those in control apart from the fact that one group has been treated and the other not. The units in the treatment and control group are in this scenario comparable (D. B. Rubin, 1974). The ATE can thus be properly isolated and identified by taking the observed difference between treatment and control groups’ outcome means; or the difference in mean militant violence between the administrative agencies that experienced a drone strikes and the administrative agencies that did not. If drone strikes is a binary variable, this can be written more formally as:

𝔼 𝑌_! 1 −𝑌_! 0 =𝔼 𝑌_! 1 𝐴_! =1 −𝔼 𝑌_! 0 𝐴_! =0

=𝔼 𝑌_! 𝐴_! =1 −𝔼 𝑌_! 𝐴_! =0 =𝔼 𝑌_! 𝑎 −𝔼 𝑌_! 𝑎^∗

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One criticism of statistical applied work is that it hasn’t been able to properly identify different causal pathways from cause to outcome. Although we may be able to estimate whether a cause statistically affects an outcome, we cannot conclude how and why the effect emerge.

As emphasised in the past sections, it is in this thesis of theoretical interest to investigate both the total causal effect of drone strikes on militant violence, and how much of this effect is moderated or accentuated through the killing of civilians. I have argued that looking at these effects provides a better test of the proposed theory, than for example looking at the alleged militant death tally. To thoroughly test these

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theoretical propositions I therefore estimate the effect of drone strikes on the level of militant violence through the number of civilian casualties. The causal structure of such a single shot setup is shown in Figure 1.

Following the counterfactual model, the intermediate covariate counting the number of the reported civilian casualties is in Figure 1 denoted 𝑀_!. The reported number of civilian casualties of drone strikes will logically most likely be affected by the actual occurrence of a drone strike. This gives the notation 𝑀_! = 𝑀_!(𝑎_!). Given that treatment 𝐴_! in this case also is binary, a drone strike either occurs or it does not, two potential outcomes for the intermediate variable the number civilian casualties exist,

𝑀_! 1 , and 𝑀_! 0 . So if a drone fires missiles on a target in an administrative agency 𝑖,

𝑀_! 1 is observed for that agency, and if not, we observe 𝑀_!(0). Crucially, the

inclusion of a mediator now makes the observed outcome dependent on both treatment and mediator, giving the potential outcome 𝑌_! 𝑎,𝑚 . This would be the level of militant violence for the administrative agency 𝑖 if drone strikes were set to treatment status 𝑎 and the reported number of civilian casualties to mediator status 𝑚. However, as we only observe one of two potential outcomes, we cannot observe administrative agency i at the same time both being struck by drones and not being struck by drones, the

observed outcome 𝑌_! equals 𝑌_! 𝑎,𝑀_! 𝑎 . So to overcome this fundamental problem of causal inference, we must therefore also here rely on aggregated average effects.

Following the discussion above, one way to define an average mediation effect/

indirect effect is:

𝐴𝑁𝐼𝐸 𝑎 =𝔼 𝑌_! 𝑎,𝑀_! 1 −𝑌_! 𝑎,𝑀_! 0 ¹² (2)

with a binary treatment. This is also called an average natural indirect effect (ANIE) and represents the average indirect effect of treatment on the outcome through a mediating variable (Greenland and Robins, 1992; Pearl, 2001). In words, it shows the change in outcome that would occur if the mediator is changed from the value that would be realised under the control state, 𝑀_!(0), to the value that would be observed under treatment 𝑀_!(1), holding treatment fixed at a particular level 𝑎, averaged over all the agencies.

12 Notation based on Imai et al. (2010)

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In a single-shot framework, the unit specific 𝑁𝐼𝐸_! 1 would in this study thus represent the difference in potential levels of militant violence over the reported number of civilians killed by drone strikes, for agency 𝑖 that were hit by drone strikes.

𝑌_! 1,𝑀_! 1 would for example denote the level of militant violence if drone missiles actually struck administrative agency 𝑖 and the corresponding number of civilian casualties. 𝑌_! 1,𝑀_! 0 would then signify the level of militant violence that would result if administrative agency 𝑖 were hit by drone strikes but the mediator, the reported number of civilians killed, taking the value it would have had if the drone hadn’t struck.

In the same way, the unit specific 𝑁𝐼𝐸_! 0 would stand for an impact on the estimated level of militant violence in administrative agency 𝑖 due to the change in the number of civilians killed by drone strikes, with the direct effect of drone strikes suppressed.

Consulting Figure 1, a simple way to understand the NIE, is that we fix the path going from 𝐴 →𝑌 at a specific level, to properly isolate the path from drone strikes through how many civilians they kill on militant violence, 𝐴→ 𝑀→𝑌. 𝐴𝑁𝐼𝐸 𝑎 thus represent in this study the average indirect effect of the reported number of civilians killed by drone strikes on militant violence among all the administrative administrative agencies in the FATA.

Similarly, one is not always interested in the mediation effects of intermediate variables. It may also be substantially interesting to see whether drone strikes affect militant violence directly, as a cohesive and deterrent measure in itself. It is then possible to define an average natural direct effect of the treatment drone strikes:

𝐴𝑁𝐷𝐸 𝑎 =𝔼 𝑌_! 1,𝑀_! 𝑎 −𝑌_! 0,𝑀_! 𝑎 (3)

Instead of fixing the value of treatment it is here the mediator that is fixed to its unit- specific potential value (Pearl, 2001, p. 8). In this setup it is the path working through the mediator, 𝐴→ 𝑀→𝑌, which is fixed, to properly isolate the direct path from 𝐴 →𝑌. 𝐴𝑁𝐷𝐸 1 thus represent the average direct effect of drone strikes on militant violence while holding the level of the reported number civilians casualties by drone strikes at the level that would be realized if drones have struck. More succinctly, it shows the average difference in militant violence between administrative agencies that were struck by drones and administrative agencies that were not struck by drones while

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holding the mediator, the reported number of civilians killed, at the average level that would be realised when exposed to drone strikes.

The ANIE and ANDE also have the property that they sum up to the ATE. The ATE of drone strikes can thus be decomposed accordingly:

𝑌 1 −𝑌 0 =𝔼 𝑌(1,𝑀 1 −𝑌 0,𝑀 0

=𝔼 𝑌 𝑎,𝑀 1 −𝑌 𝑎,𝑀 0 +𝔼 𝑌 1,𝑀 𝑎 −𝑌 0,𝑀 𝑎

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This decomposition holds event when there are interactions and non-linearities (Pearl, 2000).

Randomised interventional analogues

However, as is noted by Pearl (2001, pp. 6–8) and further discussed in Imai, Tingley, and Yamamoto (2013), the identification of natural direct and indirect effects requires strong assumptions which often hinders their identification in applied settings. The assumptions that need to be fulfilled for the ANIE and ANDE to be identified are: the effect of the treatment 𝐴 on the outcome 𝑌 must be unconfounded; 2) the effect of the mediator 𝑀 on the outcome 𝑌 must be unconfounded; 3) the effect of the treatment 𝐴 on the mediator 𝑀 must be unconfounded; and 4) there is no effect of the treatment itself that confounds the relationship between the mediator and the outcome (Pearl, 2001, p. 6–8). In many applied settings, assumption 4) is often problematic to defend, especially for longitudinal studies, where past levels of treatment may have an effect on both the mediator and outcome.¹³

If the natural effects is not possible to identify given the strictness of assumption 4), an alternative is what may be called their randomised interventional analogues (Vanderweele and Tchetgen Tchetgen, 2014). Where the natural effects fixes the mediator for each unit 𝑖 to the level it would have had under a particular treatment status 𝑎, their randomised interventional analogues fixes the mediator to a level that is

13 Figure 2 represent such a setup. I will return to this issue when extending the single-shot framework to dynamic treatment regimes.

A song of ICC and Fire - A game of drones Assessing the effects of drone strikes - evidence from Pakistan