Workshop: Causal Machine Learning for Real-World Impact

Causal Discovery using Marginal Likelihood

Anish Dhir · Mark van der Wilk


Causal discovery is an important problem in many fields such as medicine, epidemiology, or economics. Here, causal structure is necessary to relay information about the effectiveness of treatments. Recently, causal structure has also been linked with generalisation and out of distribution generalisation in prediction tasks. This problem however, is only solvable upto a Markov equivalence class without strong assumptions. Previous work has made assumptions on the data generation process to render the causal graph identifiable. These methods fail when the data generation assumptions no longer hold. In this work, we directly algorithmise the independence of causal mechanism (ICM) assumption to achieve a flexible causal discovery algorithm. In the bivariate case, this is done by showing that independent parametrisation with independent priors encodes an ICM assumption. We show that this implies different marginal likelihoods for models of different causal directions. Using a Bayesian model selection procedure to take advantage of this, we show that our method outperforms competing methods.

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