Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
This addresses the challenge of causal inference in complex, high-dimensional data for researchers and practitioners in fields like healthcare or policy-making, representing a novel method for a known bottleneck.
The paper tackles the problem of estimating causal effects with unobserved confounding in high-dimensional, nonlinear settings by proposing the deep feature proxy variable method (DFPV), which outperforms state-of-the-art proxy causal learning methods on synthetic benchmarks and shows competitive performance in confounded bandit policy evaluation.
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder. This is achieved via two-stage regression: in the first stage, we model relations among the treatment and proxies; in the second stage, we use this model to learn the effect of treatment on the outcome, given the context provided by the proxies. PCL guarantees recovery of the true causal effect, subject to identifiability conditions. We propose a novel method for PCL, the deep feature proxy variable method (DFPV), to address the case where the proxies, treatments, and outcomes are high-dimensional and have nonlinear complex relationships, as represented by deep neural network features. We show that DFPV outperforms recent state-of-the-art PCL methods on challenging synthetic benchmarks, including settings involving high dimensional image data. Furthermore, we show that PCL can be applied to off-policy evaluation for the confounded bandit problem, in which DFPV also exhibits competitive performance.