The paper argues that the causality of language models may be unnecessary or suboptimal when system behavior—extra dominant factors beyond data distribution—is treated as a first‑principle Bayesian feature. It introduces the SBD framework, incorporating system behavior into the evidence lower bound, and demonstrates a counter‑intuitive Causality Tax where ignoring these factors leads to structural error. Using a non‑causal variational family called Green Shell, the authors show through theoretical bounds, implicit measurements, and Neural Tangent Kernel analysis that this approach yields tighter error bounds and improved generalization compared to causal models.
arXiv:2609.36771v1 Announce Type: cross Abstract: Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a sys...
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...
arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.
ProximalFM is a transformer‑based model that uses prior‑data fitted networks (PFNs) to perform Bayesian proximal causal inference under hidden confounding. By training on synthetic data generated from structural causal models with oracle counterfactuals, it amortizes the Bayesian operator inversion into a single forward pass, producing posterior estimates of the conditional average treatment effect (CATE). The approach consistently outperforms prior methods across various proximal regimes, especially when latent confounding is strong and proxy variables are weakly informative, and it requires no dataset‑specific tuning.
arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2609. 26290v1 Announce Type: cross Abstract: Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage instead of directly encoding the repeated-sample response needed in a fixed deployment population.
arXiv:2609.37664v1 Announce Type: new Abstract: Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training dat...
arXiv:2609.36337v1 Announce Type: new Abstract: Tabular foundation models achieve strong performance by conditioning on labelled examples in context, but softmax attention limits their use on large d...
arXiv:2608. 09696v1 Announce Type: new Abstract: Predicting the answer to interventional ``what if'' questions --- the outcome of an action never taken --- requires a \emph{mechanistic}, causal model, not a curve fit; and learning such a model requires \emph{experiments}, because passive data leaves its mechanisms unidentified.
The paper investigates federated learning for linear non‑Gaussian acyclic models (LiNGAM), proposing the FedRCD family of algorithms that use higher‑order cumulants to enable privacy‑preserving causal discovery across distributed clients. It addresses limitations of existing federated methods, such as FedISHC’s failure under near‑symmetric noise, and introduces variants that balance communication rounds with algebraic noise handling. Experiments reveal that cumulant‑based federated approaches rank variables by a variance ladder induced by the DAG rather than by population asymmetry, and that marginal standardisation degrades performance while scale‑invariant DirectLiNGAM remains robust.