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 towards 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.