Causal inference using regression-based statistical control: Confusion in Econometrics  

作　　者：Fan Chao Guang Yu 

机构地区：[1]School of Management,Harbin Institute of Technology,Harbin 150001,China

出　　处：《Journal of Data and Information Science》2023年第1期21-28,共8页数据与情报科学学报（英文版）

基　　金：This research was funded by the National Natural Science Foundation of China(Grant No.72074060).

摘　　要：Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.

关 键 词：Causal Inference Regression Observational Studies ECONOMETRICS Causal Model 

分 类 号：F224.0[经济管理—国民经济]