Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
arXiv:2603.19186v3 Announce Type: replace Abstract: 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
The increasing availability of large observational datasets and the rapid advancements in AI for embedding and alignment techniques are making such approaches feasible and necessary.
This development improves the accuracy and reliability of treatment effect estimation by bridging the data gap between gold-standard RCTs and large observational studies (OS), enabling more robust causal inference.
The ability to integrate disparate datasets with covariate mismatch more effectively reduces the dependency on purely RCT-based evidence for complex causal questions, leading to better-informed decisions in fields like medicine and policy.
- · Healthcare research
- · AI developers
- · Pharmaceutical industry
- · Policy makers
- · Traditional statistical methods
- · Organizations relying solely on RCTs
Improved statistical methods for combining RCTs and observational studies.
Faster and more cost-effective development of new treatments and interventions by leveraging existing data.
Potentially more democratized access to advanced causal inference capabilities for smaller research groups.
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Read at arXiv cs.LG