Tuning Causal Discovery Algorithms
Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou. Tuning Causal Discovery Algorithms. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research, 2020.
Therearenumerousalgorithmsproposedintheliteratureforlearningcausalgraphicalprobabilistic models. Each one of them is typically equipped with one or more tuning hyper-parameters. The choice of optimal algorithm and hyper-parameter values is not universal; it depends on the size of thenetwork,thedensityofthetruecausalstructure,thesamplesize,aswellasthemetricofquality of learning a causal structure. Thus, the challenge to a practitioner is how to “tune” these choices, giventhatthetruegraphisunknownandthelearningtaskisunsupervised. Inthepaper,weevaluate two previously proposed methods for tuning, one based on stability of the learned structure under perturbations of the input data and the other based on balancing the in-sample ﬁtting of the model with the model complexity. We propose and comparatively evaluate a new method that treats a causal model as a set of predictive models: one for each node given its Markov Blanket. It then tunes the choices using out-of-sample protocols for supervised methods such as cross-validation. The proposed method performs on par or better than the previous methods for most metrics.