DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

ICML 2020 Nathan Kallus

We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inappropriate covariate representations, respectively... (read more)

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