CALM learns embeddings to align mismatched covariates between RCTs and observational studies, transfers outcome models, and calibrates them on trial data to improve CATE estimation without imputation.
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Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.
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Improving RCT-Based CATE Estimation Under Covariate Mismatch via Calibrated Alignment
CALM learns embeddings to align mismatched covariates between RCTs and observational studies, transfers outcome models, and calibrates them on trial data to improve CATE estimation without imputation.