Proximal Causal Inference for Hidden Outcomes

Thomas M. Menino Convention & Exhibition Center 

A key challenge in causal inference is determining causal effects in the presence of unmeasured variables. Proxy variable methods have become a notable framework for tackling this issue. One influential line of work formulates identification as an eigendecomposition task, recovering intermediate distributions that comprise the target functional. Existing literature in causal inference has demonstrated how eigendecomposition approaches can be used to account for hidden confounding and hidden treatments. Our contribution extends this framework to hidden outcomes, identifying counterfactual distributions and deriving an influence function–based estimation strategy for causal effects. Although methods for handling missing and mismeasured outcomes exist, this work provides what we believe to be the first proximal inference results for hidden outcomes. We illustrate our estimation approach through simulation studies and anticipate results from a real-world data application soon.