Multiply-Robust Causal Change Attribution
Victor Quintas-Martinez Mohammad Taha Bahadori Eduardo Santiago Jeff Mu Dominik Janzing David Heckerman
Comparing two samples of data, we observe a change in the distribution of an
outcome variable. In the presence of multiple explanatory variables, how much
of the change can be explained by each possible cause? We develop a new
estimation strategy that, given a causal model, combines regression and
re-weighting methods to quantify the contribution of each causal mechanism. Our
proposed methodology is multiply robust, meaning that it still recovers the
target parameter under partial misspecification. We prove that our estimator is
consistent and asymptotically normal. Moreover, it can be incorporated into
existing frameworks for causal attribution, such as Shapley values, which will
inherit the consistency and large-sample distribution properties. Our method
demonstrates excellent performance in Monte Carlo simulations, and we show its
usefulness in an empirical application.