Confounding Feature Acquisition for Causal Effect Estimation
This work is significant for researchers and practitioners in causal inference who deal with costly or time-consuming acquisition of confounding variables, offering a method to prioritize data collection for more efficient average treatment effect estimation.
This paper addresses the problem of missing confounding variables in observational data for treatment effect estimation by framing it as a feature acquisition problem. They propose two strategies, covariate balancing (CB) and reducing statistical estimation error on observed factual outcome error (OE), and demonstrate that OE improves sample efficiency over baseline methods across various settings.
Reliable treatment effect estimation from observational data depends on the availability of all confounding information. While much work has targeted treatment effect estimation from observational data, there is relatively little work in the setting of confounding variable missingness, where collecting more information on confounders is often costly or time-consuming. In this work, we frame this challenge as a problem of feature acquisition of confounding features for causal inference. Our goal is to prioritize acquiring values for a fixed and known subset of missing confounders in samples that lead to efficient average treatment effect estimation. We propose two acquisition strategies based on i) covariate balancing (CB), and ii) reducing statistical estimation error on observed factual outcome error (OE). We compare CB and OE on five common causal effect estimation methods, and demonstrate improved sample efficiency of OE over baseline methods under various settings. We also provide visualizations for further analysis on the difference between our proposed methods.