The Difference Between Causal Analysis and Predictive Models: Response to “Comment on Young and Holsteen (2017)â€

Cristobal Young

Sociological Methods & Research, 2019, vol. 48, issue 2, 431-447

Abstract: 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.

Keywords: model uncertainty; model robustness; model selection; multimodel analysis; model fit (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:48:y:2019:i:2:p:431-447

DOI: 10.1177/0049124118782542

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