基于机器学习的因果推断方法研究进展

Research Progress of Causal Inference Method Based on Machine Learning

机器学习是人工智能的重要分支,在预测、分类等方面的优异表现使其成为计量经济学研究的重要工具,对因果推断方法的发展具有重要推动作用。文章对机器学习应用于因果推断计量方法的文献进行梳理后,分别从机器学习方法对因果推断计量的改进,以及机器学习在因果推断中的应用这两个方面介绍了其当前研究进展。机器学习方法不仅可以与因果推断传统计量方法相结合优化其性能,还能以"数据驱动"系统全面地考虑广泛的模型,通过提高预测效果,从而进行有效的反事实推断。最后,对机器学习在因果推断经济学领域的应用研究进行了总结和展望。

Machine learning is an important branch of artificial intelligence. Its excellent performance in forecasting and classification makes it an important tool for econometrics research and plays an important role in promoting the development of causal inference methods. This paper reviews the literature on the application of machine learning in causal inference measurement, and then introduces its current research progress from the two aspects: One is the improvement that machine learning method brings to the measurement of causal inference, and the other is the application of machine learning in causal inference. Machine learning methods can not only be combined with traditional measurement methods of causal inference to optimize their performance, but also comprehensively consider a wide range of models by the "data-driven" system to improve the prediction effect,thus conducting effective counterfactual inferences. Finally, the paper summarizes and prospects the application research of machine learning in the field of causal inference economics.