Joint Causal Inference on Observational and Experimental Datasets

We introduce joint causal inference, a powerful formulation of causal discovery over multiple datasets in which we jointly learn both the causal structure and targets of interventions from independence test results. While offering many advantages, joint causal inference induces faithfulness violations due to deterministic relations, so we extend a recently proposed constraint-based method to deal with this type of violations. A preliminary evaluation shows the benefits of joint causal inference.