PriMa-Causa is a new framework for causal prescriptive maintenance that ranks maintenance actions by their estimated interventional effect. It combines a causal reasoning layer, a parametric simulator that generates interventional ground truth, and a graph-conditioned tabular causal foundation model trained once on synthetic production‑line data. The approach achieves lower prescription error than associational baselines, especially under weak overlap and sparse context, and shows further improvement when a partially disclosed causal graph is used.
arXiv:2601.22736v3 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having...
arXiv:2601. 22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials.
ACTIVA is a transformer‑based conditional variational autoencoder designed to estimate full interventional distributions from observational data without requiring interventional data, a causal graph, or per‑task fitting. It learns a conditional latent prior that amortizes causal knowledge across diverse training tasks, enabling a single forward pass to handle new causal models drawn from the training distribution. Empirical results on synthetic and biologically realistic gene‑expression simulations show that ACTIVA outperforms correlational baselines, reduces spurious non‑descendant effects, and matches strong amortized baselines, while also validating its theoretical mixture‑of‑distributions property on observationally equivalent models.
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.
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
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: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 introduces Actionable Case-Based Feature Importance (A‑CBFI), a framework that integrates structural causal models with counterfactual recourse for tabular machine learning. A‑CBFI isolates synergistic interaction bottlenecks and releases suppressive structural locks, concentrating over 98.3% of intervention effort on diagnosed root causes. Empirical tests in finance and healthcare show a 76.9% reduction in active human intervention while keeping recourse costs comparable to exhaustive causal methods.
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:2606. 01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money.
arXiv:2605.27043v2 Announce Type: replace Abstract: Predictive models trained on observational data often fail to generalise when deployed, especially when the training data is a product of the syste...
arXiv:2610.34385v1 Announce Type: cross Abstract: Agents that act on real systems should predict what their actions will cause before acting. We study when causal structure helps a learned forward mo...