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From |
Kristian Karlson <kristian.karlson@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
re: re: Re: st: binary mediation command |

Date |
Fri, 24 Jun 2011 09:53:58 +0200 |

Let me add a bit to Ariel's suggestions. The method you choose will often depend on the question--and data--at hand. The Imai et al. approach is focused on the identification of causal indirect effects/mediation, not "descriptive" or "associational" indirects effects. The former is important if you have experimental and quasi-experimental data, while the latter often is used as a summary measure of concomitant variation in three or more variables. Sociologist S.L. Morgan has an excellent article forthcoming in Sociological Methods and Research, which discusses these issues (in a sociological debate on the origins of class inequalities in educational attainment). The methods by Erikson et al., Karlson et al., and Fairlie are "associational". They do not stipulate anything about causality (but see Morgan's article). The Imai et al. approach concerns "causal" indirect effects, and to identify these effects they assume "sequential ignorability", which means (1) that selection into treatment (independent variable) is random conditional on pre-treatment covariates, and (2) that selection into treatment is random conditional on treatment and pre-treatment covariates. While the first assumption is credible when treatment is a randomized experiment, the second assumption is restrictive and untestable (although Imai et al. suggest some simulation techniques to evaluate the robustness of results to this assumption). The weakness of this second assumption is best explained by Sobel, in his 2008 article, which I consider one of the most important contributions to this literature (http://jeb.sagepub.com/content/33/2/230.short). Sobel also develops the conditions under which we can identify causal indirect effects with randomized experiments (and these conditions are very restrictive). Other methods exist for the estimation of "causal" indirect effects: VanderWeele has some developments within the marginal structural models approach (http://journals.lww.com/epidem/Abstract/2009/01000/Marginal_Structural_Models_for_the_Estimation_of.6.aspx). This approach assumes that section is random conditional on observables, which may be an overly restrictive assumption. I do not know whether VanderWeele’s approach is implemented in Stata (but as far as I know there have been some writings on the implementation of marginal structural models in Stata). Best Kristian ------------- From "Ariel Linden, DrPH" <ariel.linden@gmail.com> To <statalist@hsphsun2.harvard.edu> Subject re: Re: st: binary mediation command Date Fri, 17 Jun 2011 09:32:47 -0700 There are several other approaches to mediation analysis. One technique that I have recently become enamored with is a non-model based approach that can accommodate any combination of data types for the mediator and outcome. This eliminates the concerns that arise with forcing a non-linear variable into a linear approach (using the standard Baron-Kenny model). This program by Imai et al is in R, but they are about to come out with a Stata version (it's not as complete as the R version but handles most jobs you'd ever need). See: http://imai.princeton.edu/projects/mechanisms.html, in particular: Imai, Kosuke, Luke Keele and Dustin Tingley (2010) A General Approach to Causal Mediation Analysis, Psychological Methods 15(4) pp. 309-334. Imai, Kosuke, Luke Keele and Teppei Yamamoto (2010) Identification, Inference, and Sensitivity Analysis for Mediation Effects, Statistical Sciences, 25(1) pp. 51-71. I hope this helps Ariel * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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