Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores
不规则识别与大量极端倾向得分下处理效应的有效推断
This paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the propensity score. In particular, it covers the case of irregularly identified treatment effect parameters. We provide limit theorems for inverse probability weighting and doubly robust estimation of causal or counterfactual parameters that do not rely on trimming approaches. By construction the limiting distributions of these…