Zoom Registration Link:https://harvard.zoom.us/meeting/register/tJwvc-GhrT0iGdeqEHR_kApKb69gVvKNgyUgAbstract: In recent decades, the social sciences have been transformed by a so-called causal revolution. Noting mere associations, it is thought, is considerably less illuminating of the social world than figuring causal relations. Primary among social scientists' targets of causal identification and inference are salient social categories such as race and sex. A diverse set of methodological approaches to causal inquiry—observational techniques, experimental designs, natural or quasi-natural experience—share an underlying theoretical basis: a counterfactual analysis of causation, operationalized as a comparison of two units that differ only in their assigned "treatment," i.e., the cause under study, and are otherwise the same across in all other respects causally relevant for the target outcome. I will argue, in this talk, that the predominant explanations for how these approaches work to identify causal effects of social categories fail. They do not, as claims the leading interpretation, "isolate" the causal effect of, say, race by presenting counterfactual contrasts that are "all else equal" but for race. Contrast cases posited by inference methods are not equal but-for race because of some formal, descriptive equivalence in their profiles. I claim that, instead, the suggestion that a pair of contrasts are equal but-for race is descendent of a substantive normative judgment that the cases should receive equal outcomes despite differences there might yet be across them.
Speakers and Presenters
Lily Hu, PhD candidate in Applied Mathematics and Philosophy at Harvard University
Co-sponsored by the Graduate School or Arts and Sciences and the School of Engineering and Applied Sciences