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.
Handbook of Statistical Methods for Precision Medicine , pages=
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Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.
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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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Debiased Counterfactual Generation via Flow Matching from Observations
Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.
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Transporting treatment effects by calibrating large-scale observational outcomes
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.