%0 Journal Article
%T The Difference Between Causal Analysis and Predictive Models: Response to ¡°Comment on Young and Holsteen (2017)¡±
%A Cristobal Young
%J Sociological Methods & Research
%@ 1552-8294
%D 2019
%R 10.1177/0049124118782542
%X The commenter¡¯s proposal may be a reasonable method for addressing uncertainty in predictive modeling, where the goal is to predict y. In a treatment effects framework, where the goal is causal inference by conditioning-on-observables, the commenter¡¯s proposal is deeply flawed. The proposal (1) ignores the definition of omitted-variable bias, thus systematically omitting critical kinds of controls; (2) assumes for convenience there are no bad controls in the model space, thus waving off the premise of model uncertainty; and (3) deletes virtually all alternative models to select a single model with the highest R 2. Rather than showing what model assumptions are necessary to support one¡¯s preferred results, this proposal favors biased parameter estimates and deletes alternative results before anyone has a chance to see them. In a treatment effects framework, this is not model robustness analysis but simply biased model selection
%K model uncertainty
%K model robustness
%K model selection
%K multimodel analysis
%K model fit
%U https://journals.sagepub.com/doi/full/10.1177/0049124118782542