I am trying to work through an issue hopefully the list can help me with. Oftentimes we are interested in estimating a quantity such as the "effect" of gender on wages. ?In one respect this is incredibly easy, gender is random within known proportions so we can just estimate the causal effect without controlling for anything (mean of male wages - mean of female wages). To add any variables would be to induce posttreatment bias and distort the causal effect of gender on wages. Plausibly though we probably aren't interested in the causal effect of gender of wages, but instead the remaining "effect" of ?gender once we have accounted for education, experience, race etc. ("the resumes are the same but the man is paid more" hypothesis). ?At this point our "effect" is no longer a causal effect because there is no clear treatment intervention, but it is a plausible quantity of interest, lets call it an 'attributable' effect. So now I raise two questions: ?1) Can we still use matching to reduce model dependence even though we recognize we are not estimating a causal effect?, 2) Can we estimate multiple attributable effects from the same model by matching first ?on one treatment variable (gender) and then on another (college education)? King and Zeng (2007) addresses this by suggesting that we estimate multiple-variable causal effects by moving two variables simultaneously. ?While this may help us get at a causal effect, as I argue above we may not actually be interested in that quantity at all. ?Of course it doesn't change the fact that the quantity has to be clearly interpretable (to use an example from the article, what does it mean to test the effect of unemployment on the duration of a dictatorship while controlling for a well-armed resistance outside the front gate of the palace?); however, this strikes me as all the more reason to use matching as there will probably be no match for low unemployment levels with well-armed resistances. ?I'd say that our counterfactual needs to be interpretable, but unfortunately I don't think "counterfactual" is the appropriate word any longer (as again, there is no plausible intervention). I raise the issue because I think these attributable effects are quite common in the literature and make a better argument for articles that include posttreatment variables. Brandon _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l

I am trying to work through an issue hopefully the list can help me with. Oftentimes we are interested in estimating a quantity such as the "effect" of gender on wages. In one respect this is incredibly easy, gender is random within known proportions so we can just estimate the causal effect without controlling for anything (mean of male wages - mean of female wages). To add any variables would be to induce posttreatment bias and distort the causal effect of gender on wages. Plausibly though we probably aren't interested in the causal effect of gender of wages, but instead the remaining "effect" of gender once we have accounted for education, experience, race etc. ("the resumes are the same but the man is paid more" hypothesis). At this point our "effect" is no longer a causal effect because there is no clear treatment intervention, but it is a plausible quantity of interest, lets call it an 'attributable' effect. So now I raise two questions: 1) Can we still use matching to reduce model dependence even though we recognize we are not estimating a causal effect?, 2) Can we estimate multiple attributable effects from the same model by matching first on one treatment variable (gender) and then on another (college education)? King and Zeng (2007) addresses this by suggesting that we estimate multiple-variable causal effects by moving two variables simultaneously. While this may help us get at a causal effect, as I argue above we may not actually be interested in that quantity at all. Of course it doesn't change the fact that the quantity has to be clearly interpretable (to use an example from the article, what does it mean to test the effect of unemployment on the duration of a dictatorship while controlling for a well-armed resistance outside the front gate of the palace?); however, this strikes me as all the more reason to use matching as there will probably be no match for low unemployment levels with well-armed resistances. I'd say that our counterfactual needs to be interpretable, but unfortunately I don't think "counterfactual" is the appropriate word any longer (as again, there is no plausible intervention). I raise the issue because I think these attributable effects are quite common in the literature and make a better argument for articles that include posttreatment variables. Brandon _______________________________________________ gov2001-l mailing list gov2001-l at lists.fas.harvard.edu http://lists.fas.harvard.edu/mailman/listinfo/gov2001-l