Jin Tian: Estimating Identifiable Causal Effects through Double Machine Learning

Jin Tian (Iowa State University): Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches Discussant: Ilya Shpitser (John Hopkins University) Abstract: Inferring causal effects from observational data is a fundamental task throughout the empirical sciences. General methods have been developed to decide the identifiability of a target effect from a combination of observational data and the causal graph underlying the system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. We aim to fill this gap between causal identification and causal estimation. In this talk, I will discuss two versions of this problem. (1) Graph-based: For any identifiable causal functionals given a causal graph, we develop a general estimator with double/debiased machine learning (DML) properties enjoying doubly robustness against model misspecification and debiasedness against biases in nuisance function estimation permitting the use of machine learning techniques for estimating nuisances. This constitutes the first general result of identification with robustness guarantees given an arbitrary causal graph. (2) Data-driven: We study causal estimation from a Markov equivalence class (MEC) of the underlying causal graphs represented by a partial ancestral graph (PAG), which is learnable from observational data. In particular, we develop a general DML estimator for any identifiable causal effects in a PAG. The result provides an entirely data-driven solution to causal estimation, i.e., from observational data - PAG by structure learning - identifiability of target effect P(y|do(x)) - estimating P(y|do(x)) from data. Joint work with Yonghan Jung and Elias Bareinboim.