Neural Doubly Robust Proximal Causal Estimation

We consider the challenging task of estimating treatment effects from observational data under the assumption that there are unobserved confounders. We employ the proximal causal estimation framework, that assumes access to control (proxy) measurements that contain information about unobserved confounders. We consider outcome and treatment bridges, which provide two distinct ways of estimating causal effects. We also consider a doubly-robust approach, based on combining the outcome and treatment bridges, which is robust in expectation to either (but not both) of the two bridge functions being misspecified. We present a new theoretical bound on the estimation accuracy of the treatment bridge, and we analyze the variance of the doubly-robust estimator. We investigate the impact of autoencoder-based regularization through an ablation study, finding that simpler models sometimes outperform more complex variants. Comparisons with state-of-the-art methods on synthetic and real-world data demonstrate the advantages of our approach.