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:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.
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.
Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...
arXiv:2609.24422v1 Announce Type: new Abstract: Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian...
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:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv:2609.40051v1 Announce Type: new Abstract: Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured....
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...
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.
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.
TabCausal is a causal discovery foundation model that learns to map datasets directly to causal graphs by pretraining across diverse causal environments. It uses a dynamic task construction strategy to expose the model to varied graph priors, mechanisms, noise models, dimensions, sample sizes, and intervention regimes, improving transferability from observational and mixed‑interventional data. On large synthetic benchmarks and a new protocol‑guided semantic benchmark, TabCausal outperforms many classical baselines and shows robust structure recovery, especially when interventional evidence is available.