Although over sixty partners have joined the US-led coalition against the Islamic State (IS), only a handful of states was willing to carry out air strikes against IS-targets. This article aims to explain the pattern of democratic participation in the air campaign. It builds on the rich literature on military burden sharing and democratic peace theory to develop a multi-causal model, which is tested with Qualitative Comparative Analysis. The results of the analysis suggest that the pattern of participation in the air strikes results from a complex interplay between alliance politics, threat perception and domestic institutional constraints. The threat posed by foreign fighters and a strong interest in a good relationship with the US constituted important incentives to participate in the air strikes, while a high level of parliamentary involvement in military deployment decisions inhibited participation. Furthermore, states that were situated in Russia's immediate vicinity refrained from participating, in spite of their strong dependence on the US' security guarantee. Lastly, the analysis did not provide convincing evidence that partisan politics had an impact on participation in the air strikes. On August 7, 2014, the United States launched its first air strikes against the Islamic State (IS). Although "Operation Inherent Resolve" started as a unilateral intervention, the Obama administration began mobilizing a broad coalition of allies as the air campaign intensified. At first glance, its efforts seem very successful: Washington managed to enlist 58 countries as members of the "global coalition to degrade and defeat ISIL" (Allen 2014). However, few allies actually committed military forces to the coalition. At the time of writing (July 2015), only thirteen countries have participated in offensive air operations. In consequence, the United States kept playing a dominant role in the campaign, carrying out the brunt of the air strikes. Many states contributed to the fight against the IS in other ways, for example, by sending arms, ammunition, or military instructors to reinforce Iraqi and Kurdish forces. However, the financial and political costs of these contributions fall far below the burdens involved in participating in combat operations. The latter not only entail more sizeable financial costs, but also a considerable risk of

This special issue addresses questions of causality and validity of different solution types in configurational comparative methods (CCMs). First, what main parameters characterize the debate about correct causal interpretation of solution types? Second, to what extent has this debate been linked to a theory of causation? The special issue contribution by Mahoney and Acosta bases qualitative comparative analysis (QCA) within a regularity theory of causation integrating type-level inferences and counterfactual cases. Swiatczak clarifies how the different algorithms underlying QCA and Coincidence Analysis (CNA) produce non-identical models. Baumgartner defines and benchmarks QCA solution types against the search target of minimal robust sufficiency. Alamos-Concha et al. identify the conservative solution as most appropriate for a multimethod design combining a counterfactual causal understanding at the cross-case level with an in-depth mechanistic explanation at the within-case level. Finally, Mahoney and Owen develop a general set-theoretic framework for the study of necessity and sufficiency in quantitative research using a counterfactual understanding of causality. Our introduction reviews the state of the art, identifies current limitations and open questions regarding the theoretical basis for causal interpretation of QCA solutions.

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Both the natural and the social sciences are currently facing a deep "reproducibility crisis". Two important factors in this crisis have been the selective reporting of results and methodological problems. In this article, we examine a fusion of these two factors. More specifically, we demonstrate that the uncritical import of Boolean optimization algorithms from electrical engineering into some areas of the social sciences in the late 1980s has induced algorithmic bias on a considerable scale over the last quarter century. Potentially affected are all studies that have used a method nowadays known as Qualitative Comparative Analysis (QCA). Drawing on replication material for 215 peer-reviewed QCA articles from across 109 highprofile management, political science and sociology journals, we estimate the extent this problem has assumed in empirical work. Our results suggest that one in three studies is affected, one in ten severely so. More generally, our article cautions scientists against letting methods and algorithms travel too easily across disparate disciplines without sufficient prior evaluation of their suitability for the context in hand.