Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
This addresses selection bias in fields like healthcare and economics, offering an incremental improvement over existing methods.
The paper tackles the problem of selection bias in counterfactual reasoning for individual treatment effects estimation by proposing a novel method that learns two groups of latent variables to separate bias-causing factors from outcome-relevant ones, achieving state-of-the-art performance on synthetic and benchmark datasets.
Counterfactual reasoning is an important paradigm applicable in many fields, such as healthcare, economics, and education. In this work, we propose a novel method to address the issue of \textit{selection bias}. We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction. They are learned by an auto-encoder where an additional regularized loss based on Pearson Correlation Coefficient (PCC) encourages the de-correlation between the two groups of random variables. This allows for explicitly alleviating selection bias by only keeping the latent variables that are relevant for estimating individual treatment effects. Experimental results on a synthetic toy dataset and a benchmark dataset show that our algorithm is able to achieve state-of-the-art performance and improve the result of its counterpart that does not explicitly model the selection bias.