Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation
因果推断中的特征选择优化：无偏估计的三阶段计算框架
Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected features in causal inference not only significantly reduce the time required to implement a matching algorithm but, more importantly, can also reduce the bias and variance when estimating causal quantities. When feature selection techniques are applied in causal inference, the crucial criterion is to select variables that, when…