Instrumental Variable Estimator for Endogenous Treatment Effect with Unknown Heterogeneity Using Sub-Propensity Score Residual

2019 ◽  
Author(s):  
Myoung‐jae Lee
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Instrumental Variable Estimator

Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles H Mullin
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Child Outcomes ◽  
Well Being ◽  
Teenage Mothers ◽  
Age At First Birth ◽  
First Birth ◽  
The Impact

AbstractEmpirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

Estimating Treatment Effects in the Presence of Noncompliance and Nonresponse: The Generalized Endogenous Treatment Model

2011 ◽  
Vol 19 (2) ◽  
pp. 205-226 ◽  
Author(s):  
Kevin M. Esterling ◽  
Michael A. Neblo ◽  
David M. J. Lazer
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Mean Squared Error ◽  
Principal Stratification ◽  
Squared Error ◽  
Treatment Effect Heterogeneity ◽  
Causal Treatment ◽  
Effect Heterogeneity ◽  
And Control

If ignored, noncompliance with a treatment or nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify and estimate causal treatment effects in the case where compliance and response depend on unobservables, we propose the parametric generalized endogenous treatment (GET) model. GET incorporates behavioral responses within an experiment to measure each subject's latent compliance type and identifies causal effects via principal stratification. Using simulation methods and an application to field experimental data, we show GET has a dramatically lower mean squared error for treatment effect estimates than existing approaches to principal stratification that impute, rather than measure, compliance type. In addition, we show that GET allows one to relax and test the instrumental variable exclusion restriction assumption, to test for the presence of treatment effect heterogeneity across a range of compliance types, and to test for treatment ignorability when treatment and control samples are balanced on observable covariates.

Propensity score specification for optimal estimation of average treatment effect with binary response

2020 ◽  
Vol 29 (12) ◽  
pp. 3623-3640
Author(s):  
John A Craycroft ◽  
Jiapeng Huang ◽  
Maiying Kong
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Optimal Estimation ◽  
Theoretical Explanation ◽  
Average Treatment Effect ◽  
Adaptive Lasso ◽  
Confounding Bias ◽  
Average Treatment ◽  
The Impact

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject’s covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database.

Impact of mobile money access on internal remittances, consumption expenditure and household welfare in Ghana

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priscilla Twumasi Baffour ◽  
Wassiuw Abdul Rahaman ◽  
Ibrahim Mohammed
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Consumption Expenditure ◽  
Content Type ◽  
Mobile Money ◽  
Money Transfer ◽  
Per Capita ◽  
Transfer Services

PurposeThe purpose of this study is to examine the impact of mobile money access on internal remittances received, per capita consumption expenditure and welfare of household in Ghana.Design/methodology/approachThe study used data from the latest round of the Ghana Living Standards Survey (GLSS 7) and employed the propensity score matching technique to estimate average treatment effect between users and non-users of mobile money transfer services.FindingsThe study finds that using mobile money is welfare enhancing, particularly for poor households and the channel by which it impacts on welfare is through higher internal remittances received and per capita expenditure. The results from the average treatment effect indicate that mobile money users receive significantly higher remittances and consequently spend averagely higher on consumption than non-users.Research limitations/implicationsAlthough the data employed in this study is limited to one country, the findings support the financial inclusion role and developmental impact of mobile money transfer services. Hence, mobile money transfer services should be promoted and facilitated by the telecommunication and financial sector regulators.Originality/valueIn addition to making original contribution to the literature on the welfare impact of mobile money, the study's use of the propensity score matching is unique.

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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