Abstract
This paper investigates ~L3-identifiability, a form of complete counterfactual identifiability within the Pearl Causal Hierarchy (PCH) framework, ensuring that all Structural Causal Models (SCMs) satisfying the given assumptions provide consistent answers to all causal questions. To simplify this problem, we introduce exogenous isomorphism and propose ~EI-identifiability, reflecting the strength of model identifiability required for ~L3-identifiability. We explore sufficient assumptions for achieving ~EI-identifiability in two special classes of SCMs: Bijective SCMs (BSCMs), based on counterfactual transport, and Triangular Monotonic SCMs (TM-SCMs), which extend ~L2-identifiability. Our results unify and generalize existing theories, providing theoretical guarantees for practical applications. Finally, we leverage neural TM-SCMs to address the consistency problem in counterfactual reasoning, with experiments validating both the effectiveness of our method and the correctness of the theory.
| Original language | English |
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| Pages (from-to) | 8022-8064 |
| Number of pages | 43 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 267 |
| State | Published - 2025 |
| Event | 42nd International Conference on Machine Learning, ICML 2025 - Vancouver, Canada Duration: 13 Jul 2025 → 19 Jul 2025 |