Tuning Causal Discovery Algorithms
Proceedings of the Tenth International Conference on Probabilistic Graphical Models, in PMLR, (2020)Abstract
There are numerous algorithms proposed in the literature for learning causal graphical probabilistic
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 the network, the density of the true causal structure, the sample size, as well as the metric of
quality of learning a causal structure. Thus, the challenge to a practitioner is how to “tune” these
choices, given that the true graph is unknown and the learning task is unsupervised. In the paper,
we evaluate two previously proposed methods for tuning, one based on stability of the learned
structure under perturbations (bootstrapping) 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.
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