scholarly journals Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-23
Author(s):  
Gabriel V. Montes-Rojas
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Nonparametric Estimation ◽  
Treatment Effects ◽  
Job Training ◽  
Estimation Procedure ◽  
Graphical Analysis ◽  
Training Dataset ◽  
Nonparametric Estimators ◽  
Quantile Treatment Effects

Nonparametric estimators for average and quantile treatment effects are constructed using Fractile Graphical Analysis, under the identifying assumption that selection to treatment is based on observable characteristics. The proposed method has two steps: first, the propensity score is estimated, and, second, a blocking estimation procedure using this estimate is used to compute treatment effects. In both cases, the estimators are proved to be consistent. Monte Carlo results show a better performance than other procedures based on the propensity score. Finally, these estimators are applied to a job training dataset.

scholarly journals Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs

2021 ◽  
pp. 1-47
Author(s):  
Liang Jiang ◽  
Xiaobin Liu ◽  
Peter C.B. Phillips ◽  
Yichong Zhang
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Tuning Parameter ◽  
Matched Pairs ◽  
Negative Dependence ◽  
Randomized Experiments ◽  
Parameter Choice ◽  
Tuning Parameters ◽  
Quantile Treatment Effects ◽  
Bootstrap Inference

Abstract This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

Dynamic Treatment Effects of Job Training

2021 ◽  
Author(s):  
Jorge Rodríguez ◽  
Fernando Saltiel ◽  
Sergio Urzúa
Keyword(s):  
Treatment Effects ◽  
Job Training ◽  
Dynamic Treatment

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 Optimal designs for nonparametric estimation of zeros of regression functions

2012 ◽  
Vol 51 (1) ◽  
pp. 55-65
Author(s):  
Zdeněk Hlávka
Keyword(s):  
Confidence Interval ◽  
Nonparametric Estimation ◽  
Regression Function ◽  
Optimal Designs ◽  
Nonparametric Estimators ◽  
Regression Functions ◽  
Expected Width

ABSTRACT We investigate nonparametric estimators of zeros of a regression function and its derivatives and we derive the distribution of design points minimizing the expected width of a confidence interval and the expected variance of the proposed estimator.

Abstract 154: Estimating Treatment Effects in More Than Two Treatment Groups via Propensity Score Weighting: Practical Guidance and Application from Anticoagulant Therapy in Atrial Fibrillation Study

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Gboyega Adeboyeje ◽  
Gosia Sylwestrzak ◽  
John Barron
Keyword(s):  
Atrial Fibrillation ◽  
Propensity Score ◽  
Anticoagulant Therapy ◽  
Observational Studies ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Multinomial Logistic Regression ◽  
Baseline Risk ◽  
Risk Scores ◽  
Treatment Groups

Background: The methods for estimating and assessing propensity scores in the analysis of treatment effects between two treatment arms in observational studies have been well described in the outcomes research methodology literature. However, in practice, the decision makers may need information on the comparative effectiveness of more than two treatment strategies. There’s little guidance on the estimation of treatment effects using inverse probability of treatment weights (IPTW) in studies where more than two treatment arms are to be compared. Methods: Data from an observational cohort study on anticoagulant therapy in atrial fibrillation is used to illustrate the practical steps involved in estimating the IPTW from multiple propensity scores and assessing the balance achieved under certain assumptions. For all patients in the study, we estimated the propensity score for the treatment each patient received using a multinomial logistic regression. We used the inverse of the propensity scores as weights in Cox proportional hazards to compare study outcomes for each treatment group Results: Before IPTW adjustment, there were large and statistically significant baseline differences between treatment groups in terms of demographic, plan type, and clinical characteristics including validated stroke and bleeding risk scores. After IPTW, there were no significant differences in all measured baseline risk factors. In unadjusted estimates of stroke outcome, there were large differences between dabigatran [Hazard ratio, HR, 0.59 (95% CI: 0.53 - 0.66)], apixaban [HR, 0.69 (CI: 0.57, 0.83)], rivaroxaban [HR, 0.60 (CI: 0.53 0.68)] and warfarin users. After IPTW, estimated stroke risk differences were significantly reduced or eliminated between dabigatran [HR, 0.89 (CI: 0.80, 0.98)], apixaban [HR, 0.92 (0.76, 1.10)], rivaroxaban [HR, 0.84 (CI: 0.75, 0.95)] and warfarin users. Conclusions: Our results showed IPTW methods, correctly employed under certain assumptions, are practical and relatively simple tools to control for selection bias and other baseline differences in observational studies evaluating the comparative treatment effects of more than two treatment arms. When preserving sample size is important and in the presence of time-varying confounders, IPTW methods have distinct advantages over propensity matching or adjustment.

scholarly journals Heteroskedasticity in One-Way Error Component Probit Models

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 35
Author(s):  
Richard Kouamé Moussa
Keyword(s):  
Monte Carlo ◽  
Robustness Analysis ◽  
Probit Model ◽  
Estimation Procedure ◽  
Error Component ◽  
Ratio Test ◽  
Probit Models ◽  
Power Of The Test ◽  
Individual Effects ◽  
High Degree

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.

scholarly journals Estimating Treatment Effects of Unit-Based Pricing of Household Solid Waste Disposal

2018 ◽  
Vol 48 (1) ◽  
pp. 21-43
Author(s):  
Christopher Wright ◽  
John M. Halstead ◽  
Ju-Chin Huang
Keyword(s):  
Propensity Score ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Propensity Score Matching ◽  
Waste Disposal ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Solid Waste Disposal ◽  
Annual Reduction

Propensity score matching is used to estimate treatment effects when data are observational. Results presented in this study demonstrate the use of propensity score matching to evaluate the average treatment effect of unit-based pricing of household trash for reducing municipal solid waste disposal. Average treatment effect of the treated for 34 New Hampshire communities range from an annual reduction of 631 pounds per household to 823 pounds per household. This represents an annual reduction of 42 percent to 54 percent from an average of 1530 pounds per household if a town did not adopt municipal solid waste user fees.

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