Treatment Effect Estimation of the DragonNet under Overlap Violations

Bachelor thesis (2022)

Authors

R.J. van Veen Electrical Engineering, Mathematics and Computer Science

Contributors

S.R. Bongers Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
Rafael Bidarra Computer Graphics and Visualisation - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Marco van Veen
Causal Inference Dragonnet Causal machine learning Overlap violations
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Published Date

23-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The large amounts of observational data available nowadays have sparked considerable interest in learning causal relations from such data using machine learning methods. One recent method for doing this, which provided promising results, is the DragonNet (Shi et al., 2019), which utilises neural networks in order to estimate average treatment effects in populations. The performance of the model, however, was not tested on datasets which contain low amounts of overlap between the treated and non-treated subpopulations, which makes it harder to accurately estimate treatment effects. Therefore, the goal of this paper is to investigate the performance of the DragonNet when used on datasets with (near) overlap violations. This has been done by looking at the mean absolute errors and variances of the estimated treatment effects and comparing these to other models. The results showed that the performance of the DragonNet becomes significantly worse compared to other models when large portions of the population suffer from low overlap. Additionally, the variance of the results also increases in these cases, making the results less reliable. From the obtained results, it can be concluded that it is best to choose another model for treatment effect estimation if relatively large amounts of overlap violations are suspected.