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.
By Sejong Oh
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang
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.
By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
CAFE is a new framework for automated feature engineering that treats the task as a causally‑guided sequential decision process. It first learns a sparse directed acyclic graph to identify causal relationships among features and the target, then uses a multi‑agent deep Q‑learning system to select causal groups and transformation operators while managing feature complexity. On 15 public benchmarks, CAFE outperforms leading AFE baselines, converges faster, and shows markedly lower performance drops under distribution shifts.
By Arun Vignesh Malarkkan, Wangyang Ying, Yanjie Fu
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
By Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
CausalArena is a new benchmark designed to evaluate causal discovery methods in the era of foundation models. It unifies synthetic structural causal models (SCMs), semantically grounded SCMs, and formula‑grounded SCMs, while also including real‑world datasets for external validation. Experiments show that performance rankings vary widely across different SCM families and protocols, indicating that strong results on one benchmark do not necessarily transfer to others.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye
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.
By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye
arXiv:2609.24112v1 Announce Type: cross
Abstract: Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems...
By Chenfeng Huang, Thuy T. Le, Zixuan Ma, Hien Tran
The paper evaluates four main approaches to scalable nonlinear causal discovery—differentiable, amortized, score‑matching, and combinatorial search—highlighting their distinct accuracy‑runtime trade‑offs. It introduces SPADE, a spline‑based score‑evaluation method that compiles and reuses sufficient statistics, reducing combinatorial search complexity from O(nd³) to O(nd²+d³). Empirical results show SPADE dramatically extends the scalability‑accuracy frontier, solving large‑scale problems with high structural accuracy on both synthetic and real‑world data.
By Hendrik Suhr, Sascha Xu, Jilles Vreeken
CausalArena is a unified, evolvable benchmark designed to evaluate causal discovery methods across diverse structural causal models (SCMs). It incorporates synthetic SCMs for controlled structural variation, semantic operational SCMs for human-auditable environments, and formula-grounded SCMs to test discovery under explicit scientific mechanisms, along with real-world datasets for external validity. Experiments show that performance rankings vary significantly across SCM families and protocols, indicating that strong results on one benchmark do not generalize to others, especially in the context of causal discovery foundation models.
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...
By Mallesh Madapathi
The paper investigates why causal Bayesian networks are labeled as causal, noting that interventions are defined within the model while predictions refer to real-world actions. It introduces a formal framework that separates the causal model from the data-generating process and formalizes how actions map to interventions. The authors prove an impossibility result showing that no non‑circular interpretation can satisfy all desirable properties, and they explore the consequences for falsification, causal discovery, representation learning, and abstraction.
By Frederik Hytting J{\o}rgensen, Luigi Gresele, Sebastian Weichwald