The paper introduces C$^{3}$T, a Counterfactual Causal Conversation Transformer that models sentiment shifts in social‑media conversation trees by treating discourse moves such as denial, evidence, and toxicity as interventions. It adds a causal sentiment reasoning layer, CaSiRe, to public rumor datasets, providing sentiment, shift, intervention, and causal‑source annotations. Experiments show that C$^{3}$T outperforms text‑only, graph‑based, and temporal baselines in predicting sentiment and attribution, revealing that denials and evidence reduce negativity while toxicity increases it.
arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.
By Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor, Roi Reichart
arXiv:2404.06349v3 Announce Type: replace
Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual r...
By Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan
arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.
By Yuzhe Wang, Yaochen Zhu, Jundong Li
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
The paper introduces a modular framework that builds an entity‑interaction graph from narratives without relying on annotated node labels. It uses a domain‑adapted BERT encoder to identify entity spans, constructs a bipartite graph, and classifies it with a Heterogeneous Graph Transformer. By casting the graph as a hypergraph and applying counterfactual distillation, the method estimates the Individual Treatment Effect of each node, pruning extraneous entities to produce a minimal predictive subgraph while maintaining high accuracy on the PAN Oppositional Narratives benchmark.
By Diego Revilla, Martin Fernandez-de-Retana, Lingfeng Chen, Aritz Bilbao-Jayo, Miguel Fernandez-de-Retana
arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.
By Junqi Chen, Sirui Chen, Chaochao Lu
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...
CoBe is a benchmark that evaluates a model’s ability to perform conversational counterfactual text editing, requiring the model to revise a given scenario based on a hypothetical change while preserving unaffected facts. The dataset includes 2,000 questions across domains such as healthcare, science, engineering, business, and everyday life. Experiments show that current models often default to associational generation rather than maintaining causal structure, and performance does not reliably improve with larger model size or longer outputs.
By Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar, Kai-Zhan Lee, Aurghya Maiti, Yushu Pan, Jeffrey Wu, Mingxuan Li, Elias Bareinboim
arXiv:2609.36776v1 Announce Type: new
Abstract: Emotional Video Captioning aims to generate factually accurate and emotionally empathetic descriptions. While recent methods have recognized the import...
By Cheng Ye, Weidong Chen, Peipei Song, Zhendong Mao
PubMedCausal is a new span‑level annotated corpus for causal relation extraction in biomedical text, comprising 30,000 paragraph‑level rows with 3,945 causal rows and 6,491 adjudicated cause–effect pairs. Each causal relation is annotated with full‑text cause and effect spans, causality type, and sententiality, allowing evaluation of both causal detection and full‑span extraction. Benchmarking shows biomedical encoders like PubMedBERT achieve an F1 of 0.7391 for detection, while the generative model DeepSeek‑R1‑32B reaches a Cosine Pair F1 of 0.6765 for extraction, and the corpus supports cross‑dataset transfer learning.
By Ifeoluwa Kunle-John, Josiah Paul, Oluwatosin Agbaakin, Peter Aina, Ikenna Odezuligbo, Sydney Anuyah
The study evaluates 12 instruction‑tuned open‑weight LLMs on six causal‑graph benchmarks, testing five prompting strategies and four confidence sources. Findings show that LLMs tend to over‑predict edges, misclassify indirect or reversed edges as direct, and exhibit high over‑confidence, while conventional confidence estimates are unreliable and agreement signals offer limited improvement. The results suggest LLMs should be used as externally validated soft causal priors rather than definitive causal‑structure evidence.
By Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy, Zhiqian Chen, Lei Zhang, Kaiqun Fu, Taoran Ji