scholarly journals Selection Bias Adjustment in Treatment-Effect Models as a Method of Aggregation

10.3386/t0187 ◽  
1996 ◽  
Author(s):  
Robert Moffitt
Keyword(s):  
Selection Bias ◽  
Treatment Effect ◽  
Bias Adjustment ◽  
Treatment Effect Models

Treatment Effects and Informative Missingness with an Application to Bank Recapitalization Programs

2014 ◽  
Vol 104 (5) ◽  
pp. 212-217 ◽  
Author(s):  
Angela Vossmeyer
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Sample Selection ◽  
Financial System ◽  
Bayesian Framework ◽  
Lender Of Last Resort ◽  
Treatment Effect Models ◽  
Bank Recapitalization

This article develops a Bayesian framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology is applied to a banking study that evaluates the effectiveness of lender of last resort (LOLR) policies and their ability to resuscitate the financial system. This paper employs a novel bank-level dataset from the Reconstruction Finance Corporation, and jointly models a bank's decision to apply for a loan, the LOLR's decision to approve the loan, and the bank's performance a few years after the disbursements. This framework offers practical estimation tools to unveil new answers to important regulatory questions.

Treatment Effect Deviation as an Alternative to Blinder–Oaxaca Decomposition for Studying Social Inequality

2019 ◽  
pp. 004912411985238 ◽  
Author(s):  
Weihua An ◽  
Adam N. Glynn
Keyword(s):  
Social Inequality ◽  
Selection Bias ◽  
Treatment Effect ◽  
Model Misspecification ◽  
Policy Preference ◽  
Omitted Variables ◽  
Oaxaca Decomposition ◽  
Explanatory Factors ◽  
First Case

The Blinder–Oaxaca decomposition (BOD) is a popular method for studying the contributions of explanatory factors to social inequality. The results have often been given causal interpretations. While recent work and this article both show that some types of BOD are equivalent to a counterfactual-based treatment effect/selection bias decomposition, this equivalence does not hold in general. Given this lack of general equivalence, in this article based on the counterfactual framework, we propose a method of treatment effect deviation (TED) to study social inequality. Essentially, the TED assesses to what extent the omission of particular covariates (i.e., selection bias in the omitted variables) can alter the estimated treatment effect. The TED has a better causal interpretation and can be estimated nonparametrically (and hence is more robust to model misspecification errors). Therefore, the TED may serve as an alternative to or may be used in tandem with the BOD. We illustrate the new method through two case studies. In the first case study, we show that the TED provides a more credible estimate of the treatment effect on the treated than does the BOD. However, both the TED and the BOD highlight the importance of accounting for prior earning in estimating the training effect. In the second case study, the results of the two methods differ notably, but both agree that normative factors play a significant role in generating the rural–urban disparity in social policy preference in China.

scholarly journals Please Call Again: Correcting Non-Response Bias in Treatment Effect Models

2012 ◽  
Author(s):  
Luc Behaghel ◽  
Bruno Crepon ◽  
Marc Gurgand ◽  
Thomas Le Barbanchon
Keyword(s):  
Response Bias ◽  
Treatment Effect ◽  
Treatment Effect Models

scholarly journals Please Call Again: Correcting Nonresponse Bias in Treatment Effect Models

2015 ◽  
Vol 97 (5) ◽  
pp. 1070-1080 ◽  
Author(s):  
Luc Behaghel ◽  
Bruno Crépon ◽  
Marc Gurgand ◽  
Thomas Le Barbanchon
Keyword(s):  
Treatment Effect ◽  
Nonresponse Bias ◽  
Treatment Effect Models

Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

2021 ◽  
pp. 096228022110558
Author(s):  
Steven D Lauzon ◽  
Wenle Zhao ◽  
Paul J Nietert ◽  
Jody D Ciolino ◽  
Michael D Hill ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Logistic Regression ◽  
Selection Bias ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Simulation Studies ◽  
Binary Outcome ◽  
Treatment Allocation ◽  
Analysis Phase ◽  
The Impact

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

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