arXiv AI

A Computationally Feasible Framework for Causal Probabilistic Explanation

The paper introduces Probabilistic Causal Impact (PCI), a framework that blends actual causality (AC) with Pearl’s probability of necessity and sufficiency to provide tractable, causally grounded explanations. PCI reframes explainability as an estimation problem on a probabilistic causal model, enabling efficient approximation via Monte Carlo sampling. The authors evaluate PCI on synthetic and real-world data, demonstrating consistency with AC, scalability, and applicability to complex continuous systems and large-scale causal machine learning models.

arXiv AI

Aug 20

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.

By Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Marc Schoenauer, \"Ozg\"ur \c{S}im\c{s}ek

benchmarks

arXiv Machine Learning

Aug 27

Towards A Unified Information Bottleneck Framework for Time Series Explanations

arXiv:2608. 25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior.

By Xu Zheng, Zichuan Liu, Zhuomin Chen, Mayur Akewar, Janki Bhimani, Jason Liu, Mo Sha, Jingchao Ni, Wei Cheng, Dongsheng Luo

benchmarks safety

Hugging Face Trending Papers

Aug 26

Towards A Unified Information Bottleneck Framework for Time Series Explanations

Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods...

benchmarks safety

arXiv AI

Jun 24

An Introduction to Causal Reinforcement Learning

arXiv:2606. 24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.

By Elias Bareinboim, Junzhe Zhang, Sanghack Lee

agents reinforcement-learning

arXiv AI

Jul 10

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

arXiv:2607. 08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use.

By Andrej Leban, Yuekai Sun

llms agents benchmarks

Hugging Face Trending Papers

Jun 23

An Introduction to Causal Reinforcement Learning

Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.

agents reinforcement-learning

Hugging Face Trending Papers

Jul 14

Solution of the Hempel's statistical ambiguity problem and Causal AI

This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl Hempel proposed the Requirement of Maximal Specificity (RMS) for the statistical laws used in the inference.

arXiv AI

Aug 11

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms agents benchmarks

arXiv Machine Learning

Sep 23

xWhyL: Causal Interactive Learning

arXiv:2609.26037v1 Announce Type: new Abstract: Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for lear...

By Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig, Matej Ze\v{c}evi\'c, Lavdim Halilaj, Juergen Luettin, Kristian Kersting

arXiv AI

Jul 15

Solution of the Hempel's statistical ambiguity problem and Causal AI

arXiv:2607. 12826v1 Announce Type: new Abstract: This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws.

By Evgenii Vityaev

arXiv Machine Learning

Aug 10

Adversarial Causal Intervention Falsification

arXiv:2608. 06427v1 Announce Type: new Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure.

By Mojtaba Eslami

safety

arXiv AI

1d ago

Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models

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...

By Md Musfiqur Rahman, Kenneth Lee, Ziwei Jiang, Padmaja Jonnalagedda, Ruocheng Guo, Murat Kocaoglu

benchmarks

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