Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

Abstract

We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser and Bühlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.

arXiv comment18 pages, 7 figures

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• Differentiable Causal Discovery from Interventional Data
• ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
• Causal Imputation via Synthetic Interventions
• Permutation-Based Causal Structure Learning with Unknown Intervention Targets
• Interventional Experiment Design for Causal Structure Learning
• Efficient Neural Causal Discovery without Acyclicity Constraints
• Identifying Causal Structure in Dynamical Systems
• Distributional Invariances and Interventional Markov Equivalence for Mixed Graph Models
• Bayesian causal inference via probabilistic program synthesis