A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond. (arXiv:2205.10198v1 [math.ST])

Estimation of the average treatment effect (ATE) is a central problem in
causal inference. In recent times, inference for the ATE in the presence of
high-dimensional covariates has been extensively studied. Among the diverse
approaches that have been proposed, augmented inverse probability weighting
(AIPW) with cross-fitting has emerged as a popular choice in practice. In this
work, we study this cross-fit AIPW estimator under well-specified outcome
regression and propensity score models in a high-dimensional regime where the
number of features …

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