ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.
Improved inference for heterogeneous treatment effects using real-world data subject to hidden confounding
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A new hypothesis test and asymptotic lower bound detect maximum subgroup-level treatment effect bias when benchmarking observational studies against RCTs.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
citing papers explorer
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Efficient collaborative learning of the average treatment effect
ECO-ATE is a federated semiparametrically efficient estimator for the average treatment effect on a target population that incorporates summary statistics from source populations while allowing distributional shifts.
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Detecting critical treatment effect bias in small subgroups
A new hypothesis test and asymptotic lower bound detect maximum subgroup-level treatment effect bias when benchmarking observational studies against RCTs.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.