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The dramatically growing availability of observational data is beingwitnessed in various domains of science and technology, which facilitates thestudy of causal inference. However, estimating treatment effects fromobservational data is faced with two major challenges, missing counterfactualoutcomes and treatment selection bias. Matching methods are among the mostwidely used and fundamental approaches to estimating treatment effects, butexisting matching methods have poor performance when facing data with highdimensional and complicated variables. We propose a feature selectionrepresentation matching (FSRM) method based on deep representation learning andmatching, which maps the original covariate space into a selective, nonlinear,and balanced representation space, and then conducts matching in the learnedrepresentation space. FSRM adopts deep feature selection to minimize theinfluence of irrelevant variables for estimating treatment effects andincorporates a regularizer based on the Wasserstein distance to learn balancedrepresentations. We evaluate the performance of our FSRM method on threedatasets, and the results demonstrate superiority over the state-of-the-artmethods.

Matching in Selective and Balanced Representation Space for Treatment Effects Estimation