scholarly journals Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

10.3386/t0251 ◽  
2000 ◽  
Author(s):  
Keisuke Hirano ◽  
Guido Imbens ◽  
Geert Ridder
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Efficient Estimation ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Estimation of Population Average Treatment Effects in the FIRST Trial: Application of a Propensity Score-Based Stratification Approach

2017 ◽  
Vol 53 (4) ◽  
pp. 2567-2590 ◽  
Author(s):  
Jeanette W. Chung ◽  
Karl Y. Bilimoria ◽  
Jonah J. Stulberg ◽  
Christopher M. Quinn ◽  
Larry V. Hedges
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Average Treatment ◽  
Population Average ◽  
Average Treatment Effects

scholarly journals Estimating the average treatment effects of nutritional label use using subclassification with regression adjustment

2014 ◽  
Vol 26 (2) ◽  
pp. 839-864
Author(s):  
Michael J Lopez ◽  
Roee Gutman
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Ordinal Scale ◽  
Limited Attention ◽  
Exposure Level ◽  
Average Treatment ◽  
Nutritional Label ◽  
Average Treatment Effects ◽  
Regression Adjustment ◽  
Label Use

Propensity score methods are common for estimating a binary treatment effect when treatment assignment is not randomized. When exposure is measured on an ordinal scale (i.e. low–medium–high), however, propensity score inference requires extensions which have received limited attention. Estimands of possible interest with an ordinal exposure are the average treatment effects between each pair of exposure levels. Using these estimands, it is possible to determine an optimal exposure level. Traditional methods, including dichotomization of the exposure or a series of binary propensity score comparisons across exposure pairs, are generally inadequate for identification of optimal levels. We combine subclassification with regression adjustment to estimate transitive, unbiased average causal effects across an ordered exposure, and apply our method on the 2005–2006 National Health and Nutrition Examination Survey to estimate the effects of nutritional label use on body mass index.

scholarly journals Estimation of Average Treatment Effects Based on Propensity Scores

2002 ◽  
Vol 2 (4) ◽  
pp. 358-377 ◽  
Author(s):  
Sascha O. Becker ◽  
Andrea Ichino
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Labor Economics ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Short Overview ◽  
Average Treatment ◽  
Matching Estimators ◽  
Average Treatment Effects ◽  
Supported Work

In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.

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