Bootstrap Inference with a Randomly Assigned Regressor: Covariance Filtering and the Limits of Marginal Resampling

Random assignment can make ordinary least squares (OLS) inference insensitive to outcome dependence, yet iid resampling can still fail because assignment and resampling need not remove the same covariance terms. With binary treatment, the iid variance target differs from the sampling variance by exa