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Development and Evaluation of Causal Models With an Application to Orthopaedic Inquiries

Fischer, Richard (2023) Development and Evaluation of Causal Models With an Application to Orthopaedic Inquiries. Master's Thesis / Essay, Artificial Intelligence.


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Several influential leaders in the field of Artificial Intelligence argue that something is missing in the current Deep Learning approach: causal understanding. In the field of causal reasoning, models that quantify the causal effect of some variable on another are developed. This thesis offers an overview of the field, a comparison of several estimators, including an extension to the X-Learner coined X-Learner++ and an ensemble consisting of three estimators. The comparison is done on the semi-synthetic IHDP dataset. Furthermore, a medical dataset to investigate the effect of prophylactic treatment on Venous Thromboembolism (VTE) incidence is analyzed and compared to a peerreviewed meta-analysis. The best-performing estimator in the IHDP dataset is the Augmented Inverse Probability Weighting (AIPW) estimator and the X-Learner++ with an error of -0.09. In the medical data set, the ensemble approach worked best when compared to the meta-analysis with an error of 0.51%. The thesis introduces the causal reasoning methodology to the realm of orthopaedics, and establishes trust by successfully emulating an existing meta-analysis. Furthermore, we establish that the estimators, and the developed error correction for the X-Learner, work well on the semi-synthetic dataset.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Jaeger, H.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 26 Jan 2023 09:39
Last Modified: 26 Jan 2023 09:39

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