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Padh, K.S. ; Li, Z. ; Casolo, C. ; Kilbertus, N.

Your Assumed DAG is Wrong And Here’s How To Deal With It.

In: (4th Conference on Causal Learning and Reasoning, CLeaR 2025, 7-9 May 2025, Lausanne). 2025. 1239-1267 (Proceedings of Machine Learning Research ; 275)
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing literature typically invokes hypothetical domain expert knowledge or causal discovery algorithms to justify this assumption. In practice, neither may propose a single DAG with high confidence. Domain experts are hesitant to rule out dependencies with certainty or have ongoing disputes about relationships; causal discovery often relies on untestable assumptions itself or only provides an equivalence class of DAGs and is commonly sensitive to hyperparameter and threshold choices. We propose an efficient, gradient-based optimization method that provides bounds for causal queries over a collection of causal graphs—compatible with imperfect prior knowledge—that may still be too large for exhaustive enumeration. Our bounds achieve good coverage and sharpness for causal queries such as average treatment effects in linear and non-linear synthetic settings as well as on real-world data. Our approach aims at providing an easy-to-use and widely applicable rebuttal to the valid critique of ‘What if your assumed DAG is wrong?’.
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Publication type Article: Conference contribution
Keywords Bounding ; Causal Inference ; Cause-effect Estimation ; Graphical Model
Conference Title 4th Conference on Causal Learning and Reasoning, CLeaR 2025
Conference Date 7-9 May 2025
Conference Location Lausanne
Quellenangaben Volume: 275, Issue: , Pages: 1239-1267 Article Number: , Supplement: ,
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)