English(EN)Use What You Know: Causal Foundation Models with Partial Graphs
新研究探讨因果推断和复杂数据类型的合成 · 跟踪8个来源
作者PulseAugur 编辑部·[11 个来源]·
几篇新研究论文探讨了因果推断和数据合成的进展，特别是在表格和时间数据方面。其中一篇论文引入了一个基准框架，用于根据高阶结构因果信息评估表格合成模型，突出了当前最先进模型中的不足。另一项工作提出了“提升因果推断”，以利用参数因果因子图在关系域中高效计算因果效应。此外，研究调查了因果概率时间图中的估计-预测权衡，表明仅凭预测准确性可能无法完全反映模型对因果机制的理解。进一步的工作引入了可以结合领域知识的“因果基础模型”，以及一种用于时间依赖性下因果效应的双重稳健自适应保形推断方法。
AI
arXiv:2602.22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with…
arXiv cs.LG
TIER_1English(EN)·Yufei Wu, Zhiying Gu, Alex Deng, Jacob Zhu, Linsha Chen·
arXiv:2606.30992v1 Announce Type: cross Abstract: Multicollinearity is a long lasting challenge in observational causal inference, especially in regressions -- highly correlated independent variables make it hard to isolate their individual impacts on outcomes of interest. While …
arXiv:2606.28225v1 Announce Type: new Abstract: Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty. We study this issue by characterisin…
arXiv:2606.28024v1 Announce Type: new Abstract: Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we sho…
arXiv cs.MA (Multiagent)
TIER_1English(EN)·Aniq Ur Rahman·
Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty. We study this issue by characterising an inherent estimation--prediction tradeoff in…
Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we show how lifting can be applied to efficiently comp…
arXiv cs.LG
TIER_1English(EN)·Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf·
arXiv:2602.14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortising causal discovery and infer…
arXiv:2606.30500v1 Announce Type: new Abstract: We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.
We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.
arXiv stat.ML
TIER_1English(EN)·Anna Guo, Lin Liu, David Benkeser, Razieh Nabi·
arXiv:2512.19861v2 Announce Type: replace-cross Abstract: Unmeasured confounding can render identification strategies based on adjustment functionals invalid. We study the "Napkin" graph, a causal structure that encapsulates features of M-bias, instrumental variables, and classic…
