Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study

2007 ◽  
Vol 26 (4) ◽  
pp. 754-768 ◽  
Author(s):  
Peter C. Austin ◽  
Paul Grootendorst ◽  
Sharon-Lise T. Normand ◽  
Geoffrey M. Anderson
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Treatment Effect ◽  
Monte Carlo Study ◽  
Biased Estimation

Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

2012 ◽  
Vol 11 (3) ◽  
pp. 222-229 ◽  
Author(s):  
Etienne Gayat ◽  
Matthieu Resche-Rigon ◽  
Jean-Yves Mary ◽  
Raphaël Porcher
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Propensity Score ◽  
Survival Data ◽  
Proportional Hazards ◽  
Monte Carlo Study ◽  
Proportional Hazards Models

scholarly journals Optimal Caliper Width for Propensity Score Matching of Three Treatment Groups: A Monte Carlo Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81045 ◽  
Author(s):  
Yongji Wang ◽  
Hongwei Cai ◽  
Chanjuan Li ◽  
Zhiwei Jiang ◽  
Ling Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Monte Carlo Study ◽  
Treatment Groups

Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches

2021 ◽  
pp. 0193841X2110202
Author(s):  
Walter L. Leite ◽  
Burak Aydin ◽  
Dee D. Cetin-Berber
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Missing Data ◽  
Propensity Score ◽  
Simulation Study ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Monte Carlo Simulation Study ◽  
Score Analysis

Background: Propensity score analysis (PSA) is a popular method to remove selection bias due to covariates in quasi-experimental designs, but it requires handling of missing data on covariates before propensity scores are estimated. Multiple imputation (MI) and single imputation (SI) are approaches to handle missing data in PSA. Objectives: The objectives of this study are to review MI-within, MI-across, and SI approaches to handle missing data on covariates prior to PSA, investigate the robustness of MI-across and SI with a Monte Carlo simulation study, and demonstrate the analysis of missing data and PSA with a step-by-step illustrative example. Research design: The Monte Carlo simulation study compared strategies to impute missing data in continuous and categorical covariates for estimation of propensity scores. Manipulated conditions included sample size, the number of covariates, the size of the treatment effect, missing data mechanism, and percentage of missing data. Imputation strategies included MI-across and SI by joint modeling or multivariate imputation by chained equations (MICE). Results: The results indicated that the MI-across method performed well, and SI also performed adequately with smaller percentages of missing data. The illustrative example demonstrated MI and SI, propensity score estimation, calculation of propensity score weights, covariate balance evaluation, estimation of the average treatment effect on the treated, and sensitivity analysis using data from the National Longitudinal Survey of Youth.

scholarly journals How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study

2015 ◽  
Vol 50 (5) ◽  
pp. 520-532 ◽  
Author(s):  
Patricia Rodríguez De Gil ◽  
Aarti P. Bellara ◽  
Rheta E. Lanehart ◽  
Reginald S. Lee ◽  
Eun Sook Kim ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Propensity Score ◽  
Monte Carlo Study ◽  
Propensity Score Methods

Quantile treatment effect and double robust estimators

2017 ◽  
Vol 44 (4) ◽  
pp. 585-604 ◽  
Author(s):  
Francesco Caracciolo ◽  
Marilena Furno
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Short Term ◽  
Content Type ◽  
Double Robust ◽  
Monte Carlo Studies ◽  
And Control ◽  
The Impact

Purpose Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue. Design/methodology/approach The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect. Findings Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South. Originality/value The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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