arXiv Machine Learning
DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
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Jun 17
FoundCause: Causal Discovery with Latent Confounders from Observational Data
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 Machine Learning
Jun 26
Use What You Know: Causal Foundation Models with Partial Graphs
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
arXiv Machine Learning
Jun 5
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv Machine Learning
Jul 14
Causal Foundation Models with Continuous Treatments
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
arXiv Machine Learning
Aug 4
Large Causal Models for Temporal Causal Discovery
arXiv:2602. 18662v2 Announce Type: replace Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset.
arXiv Machine Learning
Oct 8
Causal Posterior Estimation
The paper introduces Causal Posterior Estimation (CPE), a new Bayesian inference technique for simulator models where the likelihood is intractable but simulation is easy. CPE uses flow matching to approximate the posterior while embedding the model’s conditional dependencies directly into the neural network architecture. Experiments show that this hard‑coded structure yields highly accurate inference, matching or surpassing current state‑of‑the‑art methods.