High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained

In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.

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Often, observational data alone is not enough to uniquely identify a system's causal structure.

This work is also the first effort to benchmark the stability of causal discovery algorithms with respect to the values of their hyperparameters.

We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.