Estimation of average treatment effects among multiple treatment groups by using an ensemble approach

2019 ◽  
Vol 38 (15) ◽  
pp. 2828-2846 ◽  
Author(s):  
Xiaofang Yan ◽  
Younathan Abdia ◽  
Somnath Datta ◽  
K. B. Kulasekera ◽  
Beatrice Ugiliweneza ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Ensemble Approach ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors

2020 ◽  
Vol 29 (05) ◽  
pp. 2050005
Author(s):  
Lev V. Utkin ◽  
Mikhail V. Kots ◽  
Viacheslav S. Chukanov ◽  
Andrei V. Konstantinov ◽  
Anna A. Meldo
Keyword(s):  
Treatment Effects ◽  
Regression Function ◽  
Simple Modification ◽  
Conditional Average ◽  
Treatment Groups ◽  
Feature Vectors ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
The Difference ◽  
New Feature

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.

The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia

2021 ◽  
Author(s):  
Mateus C. R. Neves ◽  
Felipe De Figueiredo Silva ◽  
Carlos Otávio Freitas
Keyword(s):  
Travel Time ◽  
Treatment Effect ◽  
Agricultural Production ◽  
Treatment Effects ◽  
Geographic Information ◽  
Average Treatment Effect ◽  
Extension Services ◽  
Average Treatment ◽  
Andean Countries ◽  
Average Treatment Effects

In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

scholarly journals Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Subgroup Analysis

Healthcare ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Lorraine Johnson ◽  
Mira Shapiro ◽  
Jennifer Mankoff
Keyword(s):  
Lyme Disease ◽  
Treatment Effects ◽  
Group Analysis ◽  
Treatment Protocol ◽  
Medical Community ◽  
Patient Registries ◽  
Average Treatment ◽  
Individualized Care ◽  
Patient Reported ◽  
Average Treatment Effects

Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that sub-group analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.

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 Estimating Conditional Average Treatment Effects

2015 ◽  
Vol 33 (4) ◽  
pp. 485-505 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu ◽  
Robert P. Lieli
Keyword(s):  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects
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