CausalForge is presented, a framework for automated theoretical research in causal inference grounded in the Lean proof assistant, which combines Causalean, a foundational Lean library for causal inference, with CausalSmith, a self-improving agentic pipeline that selects research topics, proposes results, formalizes statements, constructs proofs, and presents the resulting artifacts for human inspection.
Counterfactual explanations have been proposed to provide actionable insights for complex black-box classifiers by suggesting “what-if” explanations that alter undesirable prediction outcomes to desired ones. Recent advances have emphasized desirable properties such as causality, plausibility, and contextual alignment;...
Zhen-Dong Wang, Diego García, J. Enguita et al.· Machine-mediated learning· 0 citations
A principled restriction of intervention admissibility via Resource-Aware SCMs (R-SCMs), which equip models with agent-controlled endogenous variables, enablement predicates evaluated against the acting agent’s observation, and consumable resources modelled by a partial commutative monoid is proposed.
Automated fact-checking systems still fall short of producing explanations that mirror the depth and structure of expert human reasoning. In this work, we propose a multi-agent framework that integrates five specialized linguistic agents covering polarization, linguistic style, argumentation, plausibility, and contextu...
Pedro Henrique de Oliveira Silva, L. Santos, L. Marinho et al.· Proceedings of the 37th ACM...· 0 citations
DeReLab is introduced, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures across default and inheritance reasoning, with formally verified ground truth at every turn, enabling controlled measurement of how models respond to confirming and disconfirming evid...
Jayanta Sadhu, S. Shahad, Kenneth Marino· 1 citation
Probabilistic Causal Impact (PCI) builds on actual causality and on Pearl's notions of probability of necessity and sufficiency, but recasts the question of explainability as an estimation problem on a probabilistic causal model that is easily approximated via Monte Carlo.
R. Urbaniak, Sam Witty, Daniel Waxman et al.· 1 citation
CausalArena is introduced, a unified and evolvable benchmark for causal discovery under a common protocol, and substantial ranking shifts across SCM families and protocols are revealed, showing that strong performance in one benchmark regime does not reliably transfer to others.
Zi-Rong Li, Si-Zhuang Liu, Tian-Zuo Wang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
