방법 비교

	Causal Mediation Analysis in Politics	동적 패널 데이터 모형
분야	Political Science	계량경제학
계열	Regression model	Regression model
기원 연도	2010	1988–1991
창시자	Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
유형	Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components	Dynamic regression / GMM estimation
원전	Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309–334.	Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.
별칭	Causal mediation, Mechanism analysis, Direct and indirect effects, Potential-outcomes mediation	dynamic panel model, panel data model with lagged dependent variable, DPD model, Arellano-Bond model
관련	5	5
요약	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?'	The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy.