Learning Causal Networks from Episodic Data

In numerous real-world domains, spanning from environmental monitoring to long-term medical studies, observations do not arrive in a single batch but rather over time in episodes. This challenges the traditional assumption in causal discovery of a single, observational dataset, not only because each episode may be a biased sample of the population but also because multiple episodes could differ in the causal interactions underlying the observed variables. We address these issues using notions of context switches and episodic selection bias, and introduce a framework for causal modeling of episodic data. We show under which conditions we can apply information-theoretic scoring criteria for causal discovery while preserving consistency. To in practice discover the causal model progressively over time, we propose the CONTINENT algorithm which, taking inspiration from continual learning, discovers the causal model in an online fashion without having to re-learn the model upon arrival of each new episode. Our experiments over a variety of settings including selection bias, unknown interventions, and network changes showcase that CONTINENT works well in practice and outperforms the baselines by a clear margin.