Empirical Analysis of Confounding Bias in Feature Representations for Average Treatment Effect Estimation

Abstract

Causal inference methods are often used for estimating the effects of an action on an outcome using observational data, which is a key task across various fields, such as medicine or economics. A number of methods make use of representation learning to try to obtain more
informative feature representations, which are then used for effect estimation. However, such feature representations can introduce confounding bias in the results if they lose confounding information contained within the original features. In this work, we evaluate an existing metric for measuring confounding bias in representations and use this metric to study two representation learning methods in terms of their biases in settings with low overlap between treated and control populations. We show that the metric is a suitable measure for the amount of confounding bias in a representation and representation learning methods that minimise this bias lead to better average treatment effect estimation in our experiments.