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

Autor/in	Young, Cristobal
Titel	The Difference between Causal Analysis and Predictive Models: Response to "Comment on Young and Holsteen (2017)"
Quelle	In: Sociological Methods & Research, 48 (2019) 2, S.431-447 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124118782542
Schlagwörter	Stellungnahme; Causal Models; Predictor Variables; Research Methodology; Ambiguity (Context); Robustness (Statistics); Computation + Suchen Sie Ihr Suchwort? Kausalanalyse; Prädiktor; Research method; Forschungsmethode; Widerstandsfähigkeit
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[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.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01