When Selection Meets Intervention: Additional Complexities in Causal Discovery
Dai, Haoyue Ng, Ignavier Sun, Jianle Tang, Zeyu Luo, Gongxu Dong, Xinshuai Spirtes, Peter Zhang, Kun
Dai, Haoyue
Ng, Ignavier
Sun, Jianle
Tang, Zeyu
Luo, Gongxu
Dong, Xinshuai
Spirtes, Peter
Zhang, Kun
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Department
Machine Learning
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Conference proceeding
Date
2025
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English
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Abstract
We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the relevant disease; A/B tests on mobile applications target existing users only, and gene perturbation studies typically focus on specific cell types, such as cancer cells. Ignoring this bias leads to incorrect causal discovery results. Even when recognized, the existing paradigm for interventional causal discovery still fails to address it. This is because subtle differences in when and where interventions happen can lead to significantly different statistical patterns. We capture this dynamic by introducing a graphical model that explicitly accounts for both the observed world (where interventions are applied) and the counterfactual world (where selection occurs while interventions have not been applied). We characterize the Markov property of the model, and propose a provably sound algorithm to identify causal relations as well as selection mechanisms up to the equivalence class, from data with soft interventions and unknown targets. Through synthetic and real-world experiments, we demonstrate that our algorithm effectively identifies true causal relations despite the presence of selection bias. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
H. Dai et al., “When Selection Meets Intervention: Additional Complexities in Causal Discovery,” International Conference on Representation Learning, vol. 2025, pp. 63411–63437, May 2025
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13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR