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Computing the effects of interventions from observational data is an important task encountered in many data-driven sciences. The problem is addressed by identifying the post-interventional distribution with an expression that involves only quantities estimable from the pre-interventional distribution over observed variables, given some knowledge about the causal structure. In this work, we relax the requirement of having a fully specified causal structure and study the identifiability of effects with a singleton intervention (X), supposing that the structure is known only up to an equivalence class of causal diagrams, which is the output of standard structural learning algorithms (e.g., FCI). We derive a necessary and sufficient graphical criterion for the identifiability of the effect of X on all observed variables. We further establish a sufficient graphical criterion to identify the effect of X on a subset of the observed variables, and prove that it is strictly more powerful than the current state-of-the-art result on this problem.
|Title of host publication||Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18)|
|Editors||Jérôme Lang LANG|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Publication status||Published - Jul 2018|
JABER, A., ZHANG, J., & BAREINBOIM, E. (2018). A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams. In J. L. LANG (Ed.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) (pp. 5024-5030). International Joint Conferences on Artificial Intelligence. https://www.ijcai.org/proceedings/2018/0697.pdf