  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.
