Permutation-based Causal Inference Algorithms with Interventions

TL;DR

This paper introduces two new permutation-based causal inference algorithms that utilize both observational and interventional data, providing consistency guarantees and applicability to non-Gaussian data, with demonstrated effectiveness on various biological datasets.

Contribution

The paper presents the first consistent, nonparametric algorithms for causal inference using combined observational and interventional data, extending the Greedy SP approach.

Findings

Algorithms are proven to be consistent under faithfulness.

Effective on simulated, protein signaling, and single-cell gene expression data.

Applicable to non-Gaussian data due to nonparametric nature.

Abstract

Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. 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 nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data.