A neural architecture that runs gradient matching before covariate matching, using a known anchor variable, is proposed for treatment effect estimation; the supporting theorem's proof has a critical gap.
Note 2 T in the post-interventional SEM is a collider, i.e., it only has incoming edges
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Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
A neural architecture that runs gradient matching before covariate matching, using a known anchor variable, is proposed for treatment effect estimation; the supporting theorem's proof has a critical gap.