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Volume 28
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Article

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

by
Bevan I. Smith
1,*,
Charles Chimedza
2 and
Jacoba H. Bührmann
1,*
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
*
Authors to whom correspondence should be addressed.
Math. Comput. Appl. 2023, 28(2), 32; https://doi.org/10.3390/mca28020032
Submission received: 18 January 2023 / Revised: 17 February 2023 / Accepted: 21 February 2023 / Published: 27 February 2023
(This article belongs to the Special Issue Current Problems and Advances in Computational and Applied Mechanics)
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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.
Keywords: treatment effects; counterfactuals; confounding treatment effects; counterfactuals; confounding

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MDPI and ACS Style

Smith, B.I.; Chimedza, C.; Bührmann, J.H. Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity. Math. Comput. Appl. 2023, 28, 32. https://doi.org/10.3390/mca28020032

AMA Style

Smith BI, Chimedza C, Bührmann JH. Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity. Mathematical and Computational Applications. 2023; 28(2):32. https://doi.org/10.3390/mca28020032

Chicago/Turabian Style

Smith, Bevan I., Charles Chimedza, and Jacoba H. Bührmann. 2023. "Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity" Mathematical and Computational Applications 28, no. 2: 32. https://doi.org/10.3390/mca28020032

APA Style

Smith, B. I., Chimedza, C., & Bührmann, J. H. (2023). Treatment Effect Performance of the X-Learner in the Presence of Confounding and Non-Linearity. Mathematical and Computational Applications, 28(2), 32. https://doi.org/10.3390/mca28020032

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