Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity

Author:

Smith Bevan I.1ORCID,Chimedza Charles2ORCID,Bührmann Jacoba H.1ORCID

Affiliation:

1. The School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa

2. School of Statistics and Actuarial Science, University of the Witwatersrand, Johannesburg 2000, South Africa

Abstract

This study critically evaluates a recent machine learning method called the X-Learner, that aims to estimate treatment effects by predicting counterfactual quantities. It uses information from the treated group to predict counterfactuals for the control group and vice versa. The problem is that studies have either only been applied to real world data without knowing the ground truth treatment effects, or have not been compared with the traditional regression methods for estimating treatment effects. This study therefore critically evaluates this method by simulating various scenarios that include observed confounding and non-linearity in the data. Although the regression X-Learner performs just as well as the traditional regression model, the other base learners performed worse. Additionally, when non-linearity was introduced into the data, the results of the X-Learner became inaccurate.

Publisher

MDPI AG

Subject

Applied Mathematics,Computational Mathematics,General Engineering

Reference45 articles.

1. Global and individual treatment effects using machine learning methods;Smith;Int. J. Artif. Intell. Educ.,2020

2. Measuring treatment effects of online videos on academic performance using difference-in-difference estimations;Smith;S. Afr. J. Ind. Eng.,2018

3. Interventions in higher education and their effect on student success: A meta-analysis;Sneyers;Educ. Rev.,2018

4. Ensemble learning for estimating individualized treatment effects in student success studies;Beemer;Int. J. Artif. Intell. Educ.,2017

5. The impact of online lecture recording on student performance;Williams;Australas. J. Educ. Technol.,2012

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