Causal Inference In Set-Relational Analysis: New Techniques and Developments

Abstract

Set relations are asymmetric forms of causation. A condition qualifies as sufficient if the outcome is present when the condition is present, but the outcome can also occur in the absence of the condition. Similarly, a condition qualifies as a necessary cause if the condition is present when the outcome is present, but the condition can also be in place in the absence of the outcome. Asymmetric causation distinguishes set relations from symmetric causation inherent to correlational cause-effect relationships. A major implication between symmetric and asymmetric causation is that tools of causal inference known from statistical analysis do not easily travel to set-relational research. In recent years, there has been an increasing interest in causal inference in set-relational research. New techniques have been proposed both within the classic domain of set-relational research and in the statistical field. The panel invites papers dealing with causal inference in set-relational research. Contributions to the panel can focus on causal claims based on the single-case (token) and the cross-case (type) level via counterfactuals, and the criterion of causes as raising the probability that the outcome occurs. We also welcome papers that propose alternative truth table algorithms to the conventional Quine-McClusky algorithm in Qualitative Comparative Analysis (QCA); that apply statistical tools and concepts to set relations, such as the potential outcomes framework; or that explore the nexus between set relations and directed acyclic graphs (DAG).

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