Xuelin Yang, Licong Lin, Susan Athey, Michael I. Jordan, Guido W. Imbens
arXiv 1 Nov 2025 · Econometrics
arXiv:2511.00727 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rajeev H Dehejia and Sadek Wahba (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 1.000 | 11 | 3 | 100% |
| 2 | Joseph S Ross, David Madigan, Kevin P Hill, David S Egilman, Yongfei… (2009) Pooled analysis of rofecoxib placebo-controlled clinical trial data: Lessons for postmarket pharmaceutical safety surveillance | 1.000 | 8 | 4 | 100% |
| 3 | Evan TR Rosenman, Guillaume Basse, Art B Owen, and Mike Baiocchi (2023) Combining observational and experimental datasets using shrinkage estimators | 0.874 | 5 | 2 | 100% |
| 4 | Shu Yang, Chenyin Gao, Donglin Zeng, and Xiaofei Wang (2023) Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation | 0.874 | 5 | 2 | 100% |
| 5 | Robert J LaLonde (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.811 | 4 | 2 | 100% |
| 6 | Shu Yang and Peng Ding (2020) Combining multiple observational data sources to estimate causal effects | 0.811 | 4 | 2 | 100% |
| 7 | James M Robins, Andrea Rotnitzky, and Lue Ping Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 0.737 | 3 | 2 | 100% |
| 8 | Leo Breiman (1996) Stacked regressions | 0.644 | 2 | 2 | 100% |
| 9 | Chenyin Gao and Shu Yang (2023) Pretest estimation in combining probability and non-probability samples | 0.644 | 2 | 2 | 100% |
| 10 | Edwin J Green and William E Strawderman (1991) A james-stein type estimator for combining unbiased and possibly biased estimators | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies | 0.644 | 2 | 2 |
| 2 | Introducing the b-value: combining unbiased and biased estimators from a sensitivity analysis perspective | 0.405 | 1 | 1 |
| 3 | TITLE | 0.405 | 1 | 1 |