Improving propensity score estimators' robustness to model misspecification using Super Learner

American Journal of Epidemiology

Volumn 181, Issue 2, 2015, Pages 108-119

  Pirracchio, Romain ^a,b,c   Petersen, Maya L ^a,b   Van Der Laan, Mark ^a,b  

^a HÔPITAL EUROPÉEN GEORGES POMPIDOU   (France)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c INSERM   (France)

Author keywords

Epidemiologic methods; Inverse probability of treatment weighting; Machine learning; Matching; Propensity score; Super Learner

Indexed keywords

COVARIANCE ANALYSIS; DATA SET; EPIDEMIOLOGY; ERROR CORRECTION; ESTIMATION METHOD; MACHINE LEARNING; NUMERICAL MODEL; REGRESSION ANALYSIS; SIMULATION;

HUMAN; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; PREDICTION; PROBABILITY; PROPENSITY SCORE; STATISTICAL MODEL; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; EPIDEMIOLOGY; STATISTICAL ANALYSIS; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

ARTIFICIAL INTELLIGENCE; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; HUMANS; PROPENSITY SCORE;

EID: 84925282729     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwu253     Document Type: Article

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