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Causal Inference
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DOI：10.1016/j.eng.2019.08.016.png)
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Abstract
En 中文
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms is one of key steps toward to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: ''Estimating average treatment effect: A brief review and beyond from Dr. Kun Kuang, ''Attribution problems in counterfactual inference from Prof. Lian Li, ''The Yule-Simpson paradox and the surrogate paradox from Prof. Zhi Geng, ''Causal potential theory from Prof. Lei Xu, ''Discovering causal information from observational data from Prof. Kun Zhang, ''Formal argumentation in causal reasoning and explanation from Profs. Beishui Liao and Huaxin Huang, ''Causal inference with complex experiments from Prof. Peng Ding, ''Instrumental variables and negative controls for observational studies from Prof. Wang Miao, and ''Causal inference with interference from Dr. Zhichao Jiang. (C) 2020 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
Keywords:
Causal inference
Instructive variables
Negative control
Causal reasoning and explanation
Causal discovery
Counterfactual inference
Treatment effect estimation
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