scholarly journals Estimating the variance of a propensity score matching estimator for the average treatment effect

2018 ◽  
Vol 4 (1) ◽  
pp. 71-96
Author(s):  
Ronnie Pingel
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Matching Estimator

scholarly journals Impact of Training of Small-Scale Dairy Farmers on Milk Production and Income in Ethiopia

2020 ◽  
Vol 24 (3) ◽  
pp. 1-8
Author(s):  
Wongel Getachew Seble ◽  
Kubota Satoko ◽  
Kanayama Toshihisa ◽  
Tiana Navalona Randrianantoandro ◽  
Hiroichi Kono
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Milk Production ◽  
Treatment Effect ◽  
Smallholder Farmers ◽  
Average Treatment Effect ◽  
Dairy Farmers ◽  
Average Treatment ◽  
Management Condition ◽  
Smallholder Dairy

This paper examined dairy husbandry training impact on milk production and milk income under smallholder farmers’ management condition. A cross-sectional survey was conducted in two districts in Ethiopia and the data was collected from a total of 180 smallholder dairy farmers (60 of the participants were trained on dairy husbandry practices). Propensity Score Matching (PSM) technique was employed to construct suitable comparable group and to calculate the average treatment effect on the treated sample. The average treatment effect on the treated shows that dairy husbandry training increased milk production, volume of milk processed and milk income by about 21.7%, 56.5% and 22.5% respectively. This study confirms that training on dairy husbandry plays great role to bring change in dairy technology adoption which further enhance milk production and milk income under smallholder farmers’ management condition. Keywords: milk income; milk production; Ethiopia; propensity score matching; smallholder dairy farmers, training

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.

scholarly journals Bootstrap bias correction for average treatment effects with inverse propensity weights

2019 ◽  
Vol 52 (2) ◽  
pp. 187-200
Author(s):  
GUBHINDER KUNDHI ◽  
MARCEL VOIA
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Bias Correction ◽  
Treatment Effects ◽  
Potential Problem ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Control Groups ◽  
Average Treatment ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

scholarly journals Inference on mean quality-adjusted lifetime using joint models for continuous quality of life process and time to event

2020 ◽  
Vol 53 (2) ◽  
pp. 165-189
Author(s):  
XIAOTIAN GAO ◽  
XINXIN DONG ◽  
CHAERYON KANG KANG ◽  
ABDUS S. WAHED
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Time To Event ◽  
Joint Models ◽  
Average Treatment ◽  
Continuous Quality ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

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