Сравнение на методи

	Causal Mediation Analysis in Politics	Динамичен панелен модел
Област	Political Science	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2010	1988–1991
Създател	Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation)	Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)
Тип	Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components	Dynamic regression / GMM estimation
Основополагащ източник	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.
Други названия	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
Свързани	5	5
Резюме	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.