Sample complexity of causal effect estimation with discrete covariates
Speaker: Chandler Squires
Abstract: Accurate estimates of causal effects play a key role in decision-making across applications such as healthcare, economics, and operations. In the absence of randomized experiments, a common approach to identifying causal effects uses covariate adjustment on a set of random variables. In this talk, I will discuss causal effect estimation via covariate adjustment in discrete distributions, focusing on finite-sample guarantees and the interplay between structure learning and causal effect estimation. To begin, I will present a new PAC bound on the worst case estimation error of covariate adjustment, which is exponential in the size of the adjustment set. Motivated by this result, I will present two constraint-based algorithms to search for smaller adjustment sets, PAC guarantees on these algorithms, and bounds on the misspecification error from violations of these constraints, which are combined into a full sample complexity analysis of the algorithms.