方法对比

	Causal Mediation Analysis in Politics	因果中介分析（自然直接效应和自然间接效应）
领域	Political Science	因果推断
方法族	Regression model	Regression model
起源年份	2010	2010
提出者	Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation)	Pearl (2001); general framework by Imai, Keele & Tingley (2010)
类型	Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components	Counterfactual causal decomposition
开创性文献	Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309–334.	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.
别名	Causal mediation, Mechanism analysis, Direct and indirect effects, Potential-outcomes mediation	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
相关	5	5
摘要	Causal mediation analysis decomposes the effect of a treatment — often a randomized experimental manipulation, such as a campaign message or an information treatment — into the part transmitted through a specified intermediate variable, the mediator, and the part operating through all other pathways. Formalized in the potential-outcomes framework by Imai, Keele, Tingley, and Yamamoto, it defines the average causal mediation effect (ACME) and the average direct effect, makes explicit the sequential-ignorability assumption required to identify them, and supplies a sensitivity analysis for when that assumption fails. It lets political scientists move beyond 'does the treatment work?' to 'why does it work?'	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.