B1184
Title: Causal effect estimation in graphical models with unmeasured confounders
Authors: Rohit Bhattacharya - Williams College (United States) [presenting]
Razieh Nabi - Emory University (United States)
Ilya Shpitser - Johns Hopkins University (United States)
Abstract: Recent developments will be discussed in (i) semiparametric estimation of causal effects in graphical models with unmeasured confounders, and (ii) the design of semiparametric tests for verifying the key identifying assumptions in such models. In particular, we discuss doubly robust estimation strategies for a class of causal graphical models defined by a simple graphical criterion on the treatment variable (this class includes the popular conditionally ignorable model and front-door model as special cases.) We then discuss two newly proposed goodness-of-fit tests, which under mild assumptions, can be used to verify the key identifying assumptions in this class of models. These tests rely on variationally independent pieces of a natural parameterization of the observed data likelihood, and have the appealing property that they require no additional modeling than what is used in the downstream semiparametric estimators. That is, the same models used to perform the pre-test can be re-used for downstream causal effect estimation. We end with a short discussion on theoretical and empirical comparisons of this approach to instrumental variable approaches to handling unmeasured confounding.
