A covariate-conditional distributional bridge identifies the ATT under non-monotonic confounding and yields a Neyman-orthogonal, semiparametrically efficient estimator.
These expressions incorporate nonlinear interactions and higher-order terms to reflect realistic com- plexity in treatment-free outcome dynamics
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On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator
A covariate-conditional distributional bridge identifies the ATT under non-monotonic confounding and yields a Neyman-orthogonal, semiparametrically efficient estimator.