方法对比

	Causal Mediation Analysis in Politics	Survey Experiment
领域	Political Science	Political Science
方法族	Regression model	Process / pipeline
起源年份	2010	2011
提出者	Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation)	Experimental political science; synthesized by Diana Mutz
类型	Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components	Randomized experiment embedded in a survey
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.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
别名	Causal mediation, Mechanism analysis, Direct and indirect effects, Potential-outcomes mediation	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
相关	5	4
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?'	A survey experiment embeds a randomized experiment inside a survey: respondents are randomly assigned to different versions of a question, frame, or stimulus, and their answers are compared to estimate a causal effect. By combining the internal validity of randomization with the representative samples and rich measurement of survey research, survey experiments — especially population-based ones — let political scientists draw causal inferences about how information, framing, or message attributes shape public attitudes and behavior.