Fit CATE Once: Model-Assisted Randomization Tests Without Sample Splitting
只拟合一次 CATE:无需样本分割的模型辅助随机化检验
Randomization tests and flexible treatment-effect models offer complementary strengths for analyzing data from randomized panel experiments: the former provide valid inference under the known assignment mechanism, while the latter can capture complex patterns of effect heterogeneity. We develop model-assisted randomization tests that combine these strengths without sample splitting. The key idea is to estimate an unsigned version of the conditional average treatment effect…