dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
Improved inference for heterogeneous treatment effects using real-world data subject to hidden confounding
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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.
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.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
citing papers explorer
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Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics
dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
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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.
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Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.