Interval estimation for treatment effects using propensity score matching.
J. Hill, and J. Reiter. Statistics in medicine, 25 (13):
2230-56(July 2006)4035<m:linebreak></m:linebreak>Propensity score.
DOI: 10.1002/sim.2277
Abstract
In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.
%0 Journal Article
%1 Hill2006
%A Hill, Jennifer
%A Reiter, Jerome P
%D 2006
%J Statistics in medicine
%K Adolescent AfricanAmericans Cognition ComputerSimulation DataInterpretation Female Humans Infant LowBirthWeight LowBirthWeight:growth&development Male Newborn Premature Premature:growth&development SocioeconomicFactors Statistical TreatmentOutcome
%N 13
%P 2230-56
%R 10.1002/sim.2277
%T Interval estimation for treatment effects using propensity score matching.
%U http://www.ncbi.nlm.nih.gov/pubmed/16220488
%V 25
%X In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.
@article{Hill2006,
abstract = {In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Hill, Jennifer and Reiter, Jerome P},
biburl = {https://www.bibsonomy.org/bibtex/2979719920b99008bae7faa371fdbef2c/jepcastel},
doi = {10.1002/sim.2277},
interhash = {409aef7311e90a0a8b32078fd4cf910d},
intrahash = {979719920b99008bae7faa371fdbef2c},
issn = {0277-6715},
journal = {Statistics in medicine},
keywords = {Adolescent AfricanAmericans Cognition ComputerSimulation DataInterpretation Female Humans Infant LowBirthWeight LowBirthWeight:growth&development Male Newborn Premature Premature:growth&development SocioeconomicFactors Statistical TreatmentOutcome},
month = {7},
note = {4035<m:linebreak></m:linebreak>Propensity score},
number = 13,
pages = {2230-56},
pmid = {16220488},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Interval estimation for treatment effects using propensity score matching.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16220488},
volume = 25,
year = 2006
}