Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data

• URL: http://arxiv.org/abs/2511.00727v1
• Date: Sat, 01 Nov 2025 22:24:16 GMT
• Title: Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
• Authors: Xuelin Yang, Licong Lin, Susan Athey, Michael I. Jordan, Guido W. Imbens,
• Abstract summary: We develop new methods to integrate experimental and observational data in causal inference.<n>A full model containing the causal parameter is obtained by minimizing a weighted combination of experimental and observational losses.<n>Experiments on real and synthetic data show the efficacy and reliability of our method.
• Score: 48.72384067821617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop new methods to integrate experimental and observational data in causal inference. While randomized controlled trials offer strong internal validity, they are often costly and therefore limited in sample size. Observational data, though cheaper and often with larger sample sizes, are prone to biases due to unmeasured confounders. To harness their complementary strengths, we propose a systematic framework that formulates causal estimation as an empirical risk minimization (ERM) problem. A full model containing the causal parameter is obtained by minimizing a weighted combination of experimental and observational losses--capturing the causal parameter's validity and the full model's fit, respectively. The weight is chosen through cross-validation on the causal parameter across experimental folds. Our experiments on real and synthetic data show the efficacy and reliability of our method. We also provide theoretical non-asymptotic error bounds.

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