gov3009-l November 2008

gov3009-l@lists.fas.harvard.edu

1 participants
2 discussions

Glynn on "Assessing the Empirical Evidence for Mechanism Specific Causal Effects"

by Justin Grimmer

Dear Applied Statistics Community, Please join us Wednesday, November 19th, when Adam Glynn—Government Department—will present his research, "Assessing the Empirical Evidence for Mechanism Specific Causal Effects". Adam provided the following abstract: Social scientists often cite the importance of mechanism specific causal knowledge, both for its intrinsic scientific value and as a necessity for informed policy. In this talk, I use counterfactual causal models to re-assess the empirical evidence for two oft cited examples from American and comparative politics: the voting habit effect that is not due to campaign attention and the effect of oil production on the likelihood of civil war onset that is due to the weakening of state capacity. Utilizing decompositions of direct and indirect effects, I discuss a number of identification strategies, and demonstrate through sensitivity and bounding analysis that the evidence for the aforementioned examples is weaker than is typically understood. The applied statistics workshop meets at 12 noon in room K-354, CGIS-Knafel (1737 Cambridge St) with a light lunch. Presentations start at 1215 pm and usually end around 130 pm. As always, all are welcome and please email me with any questions Cheers, Justin Grimmer

15 years, 5 months

Imai on "Identification and Inference in Causal Mediation Analysis"

by Justin Grimmer

Dead Applied Statistics Community, Please join us this Wednesday, November 12th when Kosuke Imai will present "Identification and Inference in Causal Mediation Analysis". Kosuke is currently a professor in the Department of Politics at Princeton University and an alum of the Harvard Government Department. He has provided the following abstract for his talk: Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines including communications, epidemiology, political science, psychology, and sociology. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal path between the treatment and outcome variables. In this paper, we first prove that under the assumption of sequential ignorability, the average causal mediation effects are nonparametrically identified. This identification result contrasts with previous studies which have concluded that the nonparametric identification of average causal mediation effects requires an additional assumption. Second, we show that under the same sequential ignorability assumption the average causal mediation effects can be identified in the linear structural equation model commonly used by applied researchers. Some practical implications of our identification result are also discussed. Third, we consider a simple nonparametric estimator of the average causal mediation effects and derive its asymptotic variance. Fourth, we offer sensitivity analyses in both parametric and nonparametric settings so that researchers can examine the robustness of their empirical findings to the violation of the sequential ignorability assumption. Finally, we analyze a randomized experiment from political psychology to illustrate the proposed methods. A paper for the talk is available here: http://imai.princeton.edu/research/files/mediation.pdf The applied statistics workshop meets at 12 noon in room K-354, CGIS-Knafel (1737 Cambridge St) with a light lunch. Presentations start at 1215 pm and usually end around 130 pm. As always, all are welcome and please email me with any questions Cheers Justin

15 years, 5 months

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gov3009-l November 2008