Joint Causal Inference from Multiple Contexts

The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets that elegantly unifies both approaches. JCI is a causal modeling approach rather than a specific algorithm, and it can be used in combination with any causal discovery algorithm that can take into account certain background knowledge. The main idea is to reduce causal discovery from multiple datasets originating from different contexts (e.g., different experimental conditions) to causal discovery from a single pooled dataset by adding a set of auxiliary context variables. JCI offers the following features: it deals with several different types of interventions in a unified fashion, it can learn intervention targets, it pools data across different datasets which improves the statistical power of independence tests, and by exploiting differences in distribution between contexts it improves on the accuracy and identifiability of the predicted causal relations. We evaluate the approach on flow cytometry data.