Data-driven algorithms for dimension reduction in causal inference

In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are o...

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Published in:Computational statistics & data analysis Vol. 105; pp. 280 - 292
Main Authors: Persson, Emma, Häggström, Jenny, Waernbaum, Ingeborg, de Luna, Xavier
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2017
Subjects:
algorithms
childhood
Covariate selection
diabetes
Kernel smoothing
Marginal co-ordinate hypothesis test
Matching
observational studies
Statistics
statistik
Type 1 diabetes mellitus
Matching
Marginal co-ordinate hypothesis test
Covariate selection
Kernel smoothing
Type 1 diabetes mellitus
ISSN:0167-9473, 1872-7352, 1872-7352
Online Access:Get full text
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Summary:In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. Data-driven algorithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness the algorithms search for minimal subsets of the covariate vector. Based, e.g., on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elimination procedure assessing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented.
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ISSN:0167-9473
1872-7352
1872-7352
DOI:10.1016/j.csda.2016.08.012