March 04, 2014

"Unifying the Counterfactual and Graphical Approaches to Causality" (Tomorrow at the Statistics Seminar)

Attention conservation notice: Late notice of an academic talk in Pittsburgh. Only of interest if you care about the places where the kind of statistical theory that leans on concepts like "the graphical Markov property" merges with the kind of analytical metaphysics which tries to count the number of possibly fat men not currently standing in my doorway.

A great division in the field of causal inference in statistics is between those who like to think of everything in terms of "potential outcomes", and those who like to think of everything in terms graphical models. More exactly, while partisans of potential outcomes tend to denigrate graphical models (*), those of us who like the latter tend to presume that potential outcomes can be read off from graphs, and hope someone will get around to showing some sort of formal equivalence.

That somebody appears to have arrived.

Thomas S. Richardson, "Unifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs)"
Abstract: Models based on potential outcomes, also known as counterfactuals, were introduced by Neyman (1923) and later popularized by Rubin (1974). Such models are used extensively within biostatistics, statistics, political science, economics, and epidemiology for reasoning about causation. Directed acyclic graphs (DAGs), introduced by Wright (1921) are another formalism used to represent causal systems. Graphs are also extensively used in computer science, bioinformatics, sociology and epidemiology.
In this talk, I will present a simple approach to unifying these two approaches via a new graph, termed the Single-World Intervention Graph (SWIG). 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. The SWIG is derived from a causal DAG via a simple node splitting transformation. I will illustrate the theory with a number of examples. Finally I show that SWIGs avoid a number of pitfalls that are present in an alternative approach to unification, based on "twin networks" that has been advocated by Pearl (2000).
Joint work with James Robins; paper, and shorter summary paper from the Causal Structure Learning Workshop at UAI 2013
Time and place: 4:30--5:30 pm on Wednesday, 5 March 2014, in Scaife Hall 125

As always, the talk is free and open to the public, whether the public follows their arrows or not.

*: I myself have heard Donald Rubin assert that graphical models cannot handle counterfactuals, or non-additive interactions between variables (particularly that they cannot handle non-additive treatments), and that their study leads to neglecting analysis-of-design questions. (This was during his talk at the CMU workshop "Statistical and Machine Learning Approaches to Network Experimention", 22 April 2013.) This does not diminish Rubin's massive contributions to statistics in general, and to causal inference in particular, but does not exactly indicate a thorough knowledge of a literature which goes rather beyond "playing with arrows". ^

Constant Conjunction Necessary Connexion

Posted at March 04, 2014 17:50 | permanent link

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