Towards Robust Causal Effect Identification Beyond Markov Equivalence

Kai Z. Teh; Kayvan Sadeghi; Terry Soo

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arXiv · 2506.15561

Towards Robust Causal Effect Identification Beyond Markov Equivalence

Kai Z. Teh·Kayvan Sadeghi·Terry Soo

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Abstract

Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold.

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BibTeX RIS

Kai Z. Teh, Kayvan Sadeghi, Terry Soo. 2025-06-18. Towards Robust Causal Effect Identification Beyond Markov Equivalence. https://arxiv.org/abs/2506.15561

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