An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.