Propensity Score Analysis with Latent Covariates: Measurement Error Bias Correction Using the Covariate's Posterior Mean, aka the "Inclusive" Factor Score

Autor/inn/en	Nguyen, Trang Quynh; Stuart, Elizabeth A.
Titel	Propensity Score Analysis with Latent Covariates: Measurement Error Bias Correction Using the Covariate's Posterior Mean, aka the "Inclusive" Factor Score
Quelle	In: Journal of Educational and Behavioral Statistics, 45 (2020) 5, S.598-636 (39 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620911920
Schlagwörter	Error of Measurement; Statistical Bias; Error Correction; Probability; Statistical Analysis; Adolescents; Longitudinal Studies; Suspension; Crime; Scores; National Longitudinal Study of Adolescent Health + Suchen Sie Ihr Suchwort? Messfehler; Korrektur; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Statistische Analyse; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Ausschluss; Schulausschluss; Crimes; Delict; Delicts; Delikt
Abstract	We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate X is measured via multiple error-prone items W, PS analysis using several proxies for X--the W items themselves, a summary score (mean/sum of the items), or the conventional factor score (i.e., predicted value of X based on the measurement model)--often results in biased estimation of the causal effect because balancing the proxy (between exposure conditions) does not balance X. We propose an improved proxy: the conditional mean of X given the combination of W, the observed covariates Z, and exposure A, denoted X[subscript WZA]. The theoretical support is that balancing X[subscript WZA] (e.g., via weighting or matching) implies balancing the mean of X. For a latent X, we estimate X[subscript WZA] by the inclusive factor score (iFS)--predicted value of X from a structural equation model that captures the joint distribution of (X,W,A) given Z. Simulation shows that PS analysis using the iFS substantially improves balance on the first five moments of X and reduces bias in the estimated causal effect. Hence, within the proxy variables approach, we recommend this proxy over existing ones. We connect this proxy method to known results about valid weighting/matching functions. We illustrate the method in handling latent covariates when estimating the effect of out-of-school suspension on risk of later police arrests using National Longitudinal Study of Adolescent to Adult Health data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01