Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model-Averaged Causal Effects
نویسندگان
چکیده
منابع مشابه
Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects.
Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of "big data" are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include ...
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Methods based on the propensity score comprise one set of valuable tools for comparative effectiveness research and for estimating causal effects more generally. These methods typically consist of two distinct stages: (1) a propensity score stage where a model is fit to predict the propensity to receive treatment (the propensity score), and (2) an outcome stage where responses are compared in t...
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We first define some notation. Let Y denote the response of interest and X denote a p-dimensional vector of covariates. Let T denote a binary indicator of treatment exposure: T = 1 if treated, T = 0 if control. (Yi,Xi, Ti), i = 1, . . . , n, is a random sample from (Y,X, T ). We further define Y (1) as the potential outcome if the subject were treated and Y (0) as the potential outcome if the s...
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Since the seminal introduction of the propensity score by Rosenbaum and Rubin, propensity-score-based (PS-based) methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the propensity score approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For situations where ...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2014
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2013.869498