Aug 2026· Machine-mediated learning· Vol 115· 0 citations· 51 references
TL;DR
The proposed ICRL-SM, a novel method for Implicit Causal Representation Learning from soft interventions using a causal mechanism switch variable, demonstrates strong results on both synthetic benchmarks and real-world image datasets, highlighting the potential of ICRL-SM to bridge the gap between theoretical identifiability and practical applicability.
SaCRL is introduced, a framework that jointly identifies the causal structure and learns the corresponding invariant representation without prior structural knowledge, and provides theoretical guarantees for structure identification, including under random-feature approximation, invariance satisfaction, and out-of-dist...
This paper organizes methods by prediction target, prediction granularity, encoder, structural decoder, and training regime to relate what each method predicts to how it uses data and simulator-based supervision, and distinguishes two questions: whether the target is identifiable under the assumed model class and wheth...
Pingchuan Ma, Rui Ding, Bojun Huang et al.· 0 citations
A closed-loop prior selection framework is proposed that casts prior injection as a budget-constrained optimization over a candidate prior pool and shows that the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.
RCA-DCM improves top-1 accuracy over the strongest baseline on both Sock Shop and Online Boutique, and when the true root cause in the causal chamber is unobserved and acts as a latent confounder, it recovers the exact root-cause set more often than any competing method.
Causal foundation models are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates.
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell· 0 citations
InfluenceField, an intervention-aware latent field inserted between the visual encoder and language decoder, is introduced, an intervention-aware latent field inserted between the visual encoder and language decoder that lifts patch features into a continuous spatial representation, propagates directed influence over m...