Abstract: Exploring the causal effects among things is a promising research topic in many fields such as statistics, computer science, econometrics, etc. This article briefly introduces the basic concepts involved in causal inference, and its three analytical frameworks: Counterfactual Framework(CF), Potential Outcomes Framework(POF) and Structural Causal Model(SCM). Firstly, we introduce the origin of causal effects according to CF. Secondly, based on the counterfactual theory, there are two analysis frameworks, called POF and SCM, and we introduce the key theories and methods respectively. The SCM explains the causal theory through mathematics and computable language, and it is a calculation model that clearly expresses hypotheses, propositions and conclusions. It quantitatively analyzes the pair of cause variables under the premise that the cause variables and effect variables are known. The potential outcome framework makes up for the missing potential results, so that the effect of observational research is close to experimental research. The SCM is a causal inference method based on graph theory. It divides events into three levels: observation, intervention, and counterfactual. Through the do-operation, the causal relationship at the intervention and counterfactual levels could be reduced to low-dimensional problems which can be solved by statistical methods. Finally, this article discusses the application scenes of causal inference in many fields today and compares the three analysis frameworks.