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Feature selection is an extensively studied technique in the machine learningliterature where the main objective is to identify the subset of features thatprovides the highest predictive power. However, in causal inference, our goalis to identify the set of variables that are associated with both the treatmentvariable and outcome (i.e., the confounders). While controlling for theconfounding variables helps us to achieve an unbiased estimate of causaleffect, recent research shows that controlling for purely outcome predictorsalong with the confounders can reduce the variance of the estimate. In thispaper, we propose an Outcome Adaptive Elastic-Net (OAENet) method specificallydesigned for causal inference to select the confounders and outcome predictorsfor inclusion in the propensity score model or in the matching mechanism.OAENet provides two major advantages over existing methods: it performssuperiorly on correlated data, and it can be applied to any matching method andany estimates. In addition, OAENet is computationally efficient compared tostate-of-the-art methods.

Feature Selection for Causal Inference from High Dimensional Observational Data with Outcome Adaptive Elastic Net