Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
交叉验证因果推断：结合实验与观察数据的方法
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…