因果推断的统计方法  

Statistical approaches for causal inference

作　　者：苗旺 刘春辰 耿直[3] Wang Miao;Chunchen Liu;Zhi Geng

机构地区：[1]北京大学光华管理学院商务统计与经济计量系,北京100871 [2]日本电气(NEC)中国研究院,北京100600 [3]北京大学数学科学学院,北京100871

出　　处：《中国科学：数学》2018年第12期1753-1778,共26页Scientia Sinica：Mathematica

基　　金：国家高技术研究发展计划(批准号:2015AA020507);国家重点基础研究发展计划(批准号:2015CB856000);国家自然科学基金(批准号:11331011,11771028和91630314)资助项目.

摘　　要：探索事物之间的因果关系和因果作用是很多科学研究的重要目的.因果推断的统计方法是利用试验性研究和观察性研究得到的数据,评价变量之间的因果作用和挖掘多个变量之间的因果关系.本文将介绍因果作用和因果关系的形式化定义,以及因果推断的两个主要统计模型:潜在结果模型和因果网络模型.本文将探讨因果作用的可识别性和因果网络的结构学习,综述有关因果推断的若干研究问题和动态.Causal inference is a permanent challenge topic in statistics,data science,and many other scientific fields.In this paper,we give an overview of statistical methods for causal inference.There are two main frameworks of causal inference:the potential outcome model and the causal network model.The potential outcome framework is used to evaluate causal effects of a known treatment or exposure variable on a given response or outcome variable.We review several commonly-used approaches in this framework for causal effect evaluation.The causal network framework is used to depict causal relationships among variables and the data generation mechanism in complex systems.We review two main approaches for structural learning:the constraintbased method and the score-based method.In the recent years,the evaluation of causal effects and the structural learning of causal networks are combined together.At the first stage,the hybrid approach learns a Markov equivalent class of causal networks from observed data;then at the second stage,it evaluates the causal effect for each causal network in the class;it also obtains a set of causal effects.The current frameworks of causal inference still have various demerits and disadvantages.We discuss these challenges and possible solutions in modern big data studies.

关 键 词：因果作用 因果网络 混杂因素 潜在结果模型 替代指标 因果推断 有向无环图 

分 类 号：O212[理学—概率论与数理统计]