Average Causal Effects from Nonrandomized Studies: A Practical Guide and Simulated Example

Autor/inn/en	Schafer, Joseph L.; Kang, Joseph
Titel	Average Causal Effects from Nonrandomized Studies: A Practical Guide and Simulated Example
Quelle	In: Psychological Methods, 13 (2008) 4, S.279-313 (35 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
DOI	10.1037/a0014268
Schlagwörter	Research Methodology; Inferences; Psychological Studies; Simulation; Attribution Theory; Statistical Analysis; Scores; Regression (Statistics); Dietetics; Emotional Disturbances; Eating Habits; Predictor Variables; Adolescents; Females; Observation + Suchen Sie Ihr Suchwort? Research method; Forschungsmethode; Inference; Inferenz; Simulation program; Simulationsprogramm; Statistische Analyse; Regression; Regressionsanalyse; Ernährungslehre; Gefühlsstörung; Ernährungsgewohnheit; Essgewohnheit; Prädiktor; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Weibliches Geschlecht; Beobachtung
Abstract	In a well-designed experiment, random assignment of participants to treatments makes causal inference straightforward. However, if participants are not randomized (as in observational study, quasi-experiment, or nonequivalent control-group designs), group comparisons may be biased by confounders that influence both the outcome and the alleged cause. Traditional analysis of covariance, which includes confounders as predictors in a regression model, often fails to eliminate this bias. In this article, the authors review Rubin's definition of an average causal effect (ACE) as the average difference between potential outcomes under different treatments. The authors distinguish an ACE and a regression coefficient. The authors review 9 strategies for estimating ACEs on the basis of regression, propensity scores, and doubly robust methods, providing formulas for standard errors not given elsewhere. To illustrate the methods, the authors simulate an observational study to assess the effects of dieting on emotional distress. Drawing repeated samples from a simulated population of adolescent girls, the authors assess each method in terms of bias, efficiency, and interval coverage. Throughout the article, the authors offer insights and practical guidance for researchers who attempt causal inference with observational data. (Contains 4 figures and 7 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10