Single World Intervention Graphs (SWIGs): A Unification of the Graphical and Counterfactual Approaches to Causality with Applications
James M. Robins - Harvard T.H. Chan School of Public Healths
Counterfactuals (aka Potential Outcomes) are extensively used in Statistics, Political Science, and Epidemiology for reasoning about causation. Causal directed acyclic graphs (DAGs) are another formalism used to reason about causation extensively used in Computer Science, Bioinformatics, Sociology, and Epidemiology. In this talk I will show how these two approaches can be unified through a new type of causal graph: the SWIG. SWIGs enable researchers from “graphical” and “counterfactual” disciplines to seamlessly learn from and communicate with one another, thereby speeding up the “causal revolution” of all disciplines. I will describe the utility of SWIGS in substantive applications in the biomedical sciences.
About the Speaker
Dr. James M. Robins is the Mitchell L. and Robin LaFoley Dong Professor of Epidemiology and Professor of Biostatistics at the Harvard Chan School of Public Health. Prof. Robins has pioneered analytic methods appropriate for drawing causal inferences and estimating optimal dynamic treatment regimes from complex observational and randomized studies with time-varying exposures or treatments. The methods are based on new classes of causal models including nondynamic and dynamic marginal structural models, and total effect, direct effect and optimal regime structural nested models. Furthermore, Professor Robins, with his collaborator Andrea Rotnitzky, introduced the methodology, now ubiquitous, of doubly robust estimation in causal inference and missing data models.
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