AutoCD: Automated Machine Learning for Causal Discovery Algorithms

Gerlise Chan, Tom Claassen, Holger Hoos, Tom Heskes, Mitra Baratchi

Keywords: Causal discovery, Automated Machine Learning, Causal tuning

Abstract: This paper studies Automated Machine Learning (AutoML) for causal discovery, the process of uncovering cause-and-effect relationships within data. Causal discovery is an unsupervised learning problem, as the target (the underlying ground truth causal model) is typically unknown. Therefore, the loss functions commonly used as an optimisation objective in AutoML systems developed for supervised learning problems are not applicable. We propose AutoCD, the first AutoML system utilising Bayesian optimisation based on a search space of causal discovery algorithms. In designing AutoCD, we study and compare the applicability of two different loss functions and post-hoc correction strategies. Additionally, based on the analysis of the performance of AutoCD, we propose an improved version called AutoCD_PC by warm-starting the search from the PC algorithm. Results from our experiments on datasets simulated from 45 graphical models demonstrate that AutoCD_PC performs better than the baselines by ranking the highest (avg. rank 3.69) compared to the best causal tuning baseline (avg. rank 5.21) and the best fine-tuned individual algorithm (avg. rank 4.36).

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Submission Number: 19