Learning priors for data-efficient causal discovery

The inference of causal relationships is a problem of fundamental interest in science. Compared to models that rely on mere correlations, causal models allow us to anticipate the effect of a change in a system. Such causal models have applications ranging from government policy making to personalized medicine. However, learning causal models from data is a challenging task, since it requires large data sets and, in some cases, the conduction of costly or invasive experiments. In this project, we propose a new method to learn causal models using less data. Our approach relies on the inclusion of prior knowledge derived from known causal relationships and meta-information related to the problem of interest. Our work is expected to increase the applicability of such methods in settings where data is scarce (e.g., biological and medical data).

Intern: 
Philippe Brouillard
Superviseur universitaire: 
Alain Tapp
Province: 
Quebec
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