Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Causal Mediation Analysis in Politics× | Modelo de Dados em Painel Dinâmico× | |
|---|---|---|
| Área≠ | Political Science | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2010 | 1988–1991 |
| Autor original≠ | Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation) | Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988) |
| Tipo≠ | Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components | Dynamic regression / GMM estimation |
| Fonte seminal≠ | Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309–334. DOI ↗ | 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. DOI ↗ |
| Outros nomes | 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 |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. |
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