Causal inference preliminary

Causal inference has been a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effects from observational data has become an appealing research direction owing to the large amount of available data and low budget requirements compared with randomized controlled trials. Embraced by the rapidly developed machine learning area, various causal effect estimation methods for observational data have emerged. In this chapter, we provide a comprehensive review of causal inference methods, including the basic definitions, assumptions, and illustrative examples. This chapter is reprinted from our work Yao et al. (ACM Trans Knowl Dis Data (TKDD) 15(5):1-46, 2021).