Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity. Large observational studies (OS) can supplement RCTs for conditional average treatment effect (CATE) estimation, but a key barrier is covariate mismatch: the two sources measure different, only partially overlapping, covariates. We propose CALM (Calibrated ALignment under covariate Mismatch), which learns embeddings that map each source's features into a common representation space. OS outcome models are transferred to the RCT embedding space and calibrated using trial data, preserving causal identification from randomization. Fini...
| Authors | Amir Asiaee, Samhita Pal |
|---|---|
| Primary category | cs.LG |
| Published | 19 March 2026 |
| Last updated at source | 9 July 2026 |
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