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B1410
Title: Characterizing and learning equivalence classes of causal DAGs under interventions Authors:  Karren Yang - MIT (United States) [presenting]
Abigail Katcoff - MIT (United States)
Caroline Uhler - Massachusetts Institute of Technology (United States)
Abstract: The problem of learning causal DAGs in the setting where both observational and interventional data is available is considered. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. It has been previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. 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 we evaluate our algorithm on simulated and biological datasets.