The Difference between Causal Analysis and Predictive Models: Response to 'Comment on Young and Holsteen (2017)'

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[superscript 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. [For "The Difference between Instability and Uncertainty: Comment on Young and Holsteen (2017)" (Adam Slez), see EJ1212204.]