Permutation-based Causal Inference Algorithms with Interventions

Causal inference using both observational and interventional data is a fundamentally important problem in real world applications. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are non-parametric, which makes them useful also for analyzing non-Gaussian data. In this talk, we present these two algorithms and their consistency guarantees, and we also analyze their performance on both simulation and real world application data.