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

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  • Cristobal Young

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.

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  • Cristobal Young, 2019. "The Difference Between Causal Analysis and Predictive Models: Response to “Comment on Young and Holsteen (2017)â€," Sociological Methods & Research, , vol. 48(2), pages 431-447, May.
    • Handle: RePEc:sae:somere:v:48:y:2019:i:2:p:431-447
    DOI: 10.1177/0049124118782542
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    References listed on IDEAS

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    2. Cantone, Giulio Giacomo, 2023. "The multiversal methodology as a remedy of the replication crisis," MetaArXiv kuhmz, Center for Open Science.
    3. Verhagen, Mark D., 2021. "A Pragmatist's Guide to Prediction in the Social Sciences," SocArXiv tjkcy, Center for Open Science.

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