Estimating Average Treatment Effects With Propensity Scores Estimated With Four Machine Learning Procedures: Simulation Results in High Dimensional Settings and With Time to Event Outcomes

2018 ◽  
Author(s):  
Kip Brown ◽  
Phil Merrigan ◽  
Jimmy Royer
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Propensity Scores ◽  
High Dimensional ◽  
Time To Event ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Simulation Results

scholarly journals On the estimation of average treatment effects with right‐censored time to event outcome and competing risks

2020 ◽  
Vol 62 (3) ◽  
pp. 751-763 ◽  
Author(s):  
Brice Maxime Hugues Ozenne ◽  
Thomas Harder Scheike ◽  
Laila Stærk ◽  
Thomas Alexander Gerds
Keyword(s):  
Competing Risks ◽  
Treatment Effects ◽  
Time To Event ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Estimation of Average Treatment Effects Based on Propensity Scores

2002 ◽  
Vol 2 (4) ◽  
pp. 358-377 ◽  
Author(s):  
Sascha O. Becker ◽  
Andrea Ichino
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Labor Economics ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Short Overview ◽  
Average Treatment ◽  
Matching Estimators ◽  
Average Treatment Effects ◽  
Supported Work

In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.

scholarly journals Double/Debiased/Neyman Machine Learning of Treatment Effects

2017 ◽  
Vol 107 (5) ◽  
pp. 261-265 ◽  
Author(s):  
Victor Chernozhukov ◽  
Denis Chetverikov ◽  
Mert Demirer ◽  
Esther Duflo ◽  
Christian Hansen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Observational Data ◽  
Treatment Effects ◽  
Nuisance Parameters ◽  
Average Treatment ◽  
Focal Parameters ◽  
Average Treatment Effects ◽  
Fitting In

Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.

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.

Sign in / Sign up

Export Citation Format

Share Document