RAMEN identifies treatment effects from multiple environments in a doubly robust manner by leveraging data heterogeneity without requiring the causal graph.
A double machine learning approach to combining experimental and observational data
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
The adaptive influence-based borrowing framework selects subsets of external controls by influence scores and chooses the subset minimizing MSE of the treatment effect estimator.
citing papers explorer
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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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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.