Possibility of using overrule to evaluate overlap in causal inference

Possibility of using overrule to evaluate overlap in causal inference: What is the performance of overrule in identifying overlap for different types of datasets?

Cheng, Shukun (TU Delft Electrical Engineering, Mathematics and Computer Science)

Krijthe, J.H. (mentor)
Karlsson, R.K.A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-07-28

Causal inference is a widely recognized concept in various domains, including medicine, for estimating the effect of a medication on a certain disease. During this estimation, overlap is commonly used to eliminate the error caused by other features. However, finding the real overlap region in practice is challenging due to the limited sample size and unknown data distribution. Therefore, some machine-learning methods have been proposed to estimate the overlap region. One such method is Overrule, a Python package proposed by Oberst et al. Overrule is based on rule-based lassification and estimates the overlap region by interpreting it as several rules across the features. However, it is still unclear how Overrule performs under different circumstances. Thus, the primary bjective of this project is to test the performance of Overrule with different datasets. To accomplish this, a series of tests are built and executed to evaluate the performance of Overrule in diverse scenarios.

Overlap
Overrule
machine-learning
Python

http://resolver.tudelft.nl/uuid:7940132c-6f0d-4b3e-ac49-0264532214f6

https://github.com/ShukunCheng/Rule-Based-Overlap-Estimator

Student theses

bachelor thesis

© 2023 Shukun Cheng