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Discovery of causal relations from observational data is essential for manydisciplines of science and real-world applications. However, unlike othermachine learning algorithms, whose development has been greatly fostered by alarge amount of available benchmark datasets, causal discovery algorithms arenotoriously difficult to be systematically evaluated because few datasets withknown ground-truth causal relations are available. In this work, we handle theproblem of evaluating causal discovery algorithms by building a flexiblesimulator in the medical setting. We develop a neuropathic pain diagnosissimulator, inspired by the fact that the biological processes of neuropathicpathophysiology are well studied with well-understood causal influences. Oursimulator exploits the causal graph of the neuropathic pain pathology and itsparameters in the generator are estimated from real-life patient cases. We showthat the data generated from our simulator have similar statistics asreal-world data. As a clear advantage, the simulator can produce infinitesamples without jeopardizing the privacy of real-world patients. Our simulatorprovides a natural tool for evaluating various types of causal discoveryalgorithms, including those to deal with practical issues in causal discovery,such as unknown confounders, selection bias, and missing data. Using oursimulator, we have evaluated extensively causal discovery algorithms undervarious settings.

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation