Single World Intervention Graphs:A simple framework for unifying graphs and potential outcomes with applications to mediation analysis
报告人： Thomas Richardson (华盛顿大学)
时间：2026-01-06 13:00-14:00
地点：北京大学杯新园77201
Abstract:
Causal models based on potential outcomes, also known as counterfactuals, were introduced by Neyman (1923) and extended to observational settings by Rubin (1974). Causal Directed Acyclic Graphs (DAGs) are another framework, originally introduced by Wright (1921), but subsequently significantly generalized and extended by Spirtes et al. (1993), Pearl (1995), and Dawid (2002), among others. In this talk I will first present a simple approach to unifying these two approaches via Single-World Intervention Graphs (SWIGs). The SWIG encodes the counterfactual independences associated with a specific hypothetical intervention on a set of treatment variables. The nodes on the SWIG are the corresponding counterfactual random variables. This represents a counterfactual model originally introduced by Robins (1986) using event trees, known as Finest Fully Randomized Causally Interpretable Structured Tree Graphs (FFRCISTG). Malinsky et al. (2019) show that this synthesis leads to a simplification of the do-calculus of Pearl (1995) that clarifies and separates the underlying concepts. Recently we have also shown that a (minimal) version of the SWIG framework is equivalent to a reformulation of the causal decision diagrams of Dawid (2021). By expanding the graph, SWIGs may also be used to describe a novel interventionist approach to mediation analysis whereby treatment is decomposed into multiple separable components. This provides a means of discussing direct effects without reference to “cross-world” independence assumptions, nested counterfactuals or interventions on the mediator. The theory preserves the dictum “no causation without manipulation” and makes questions of mediation empirically testable in future randomized controlled trials. This is joint work with James M. Robins (Harvard) and Ilya Shpitser (Johns Hopkins).
About the Speaker:
Thomas is a Professor in the Department of Statistics at University of Washington. He also serves as an Adjunct Professor in the Departments of Economics and Electrical Engineering and is a member of the eScience Steering Committee. He received his BA from the University of Oxford and earned his MS and PhD from Carnegie Mellon University. He is a Fellow of the Center for Advanced Studies in the Behavioral Sciences at Stanford University. His research interests include Graphical Models and Causality.
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