Explainable Causal

Explainable causal reasoning aims to understand and represent causal relationships within data, particularly in complex systems like large language models and medical texts, where traditional correlation-based methods fall short. Current research focuses on developing methods that leverage large language models to generate counterfactual explanations and identify causal networks, often employing techniques like adversarial learning and graph convolutional networks to address challenges posed by non-stationarity and high dimensionality. This work is significant for improving the reliability and trustworthiness of AI systems, enhancing interpretability in various domains, and facilitating more nuanced causal analysis in fields like mental health research and medical diagnosis.

Papers

May 7, 2024

A Causal Explainable Guardrails for Large Language Models
Zhixuan Chu, Yan Wang, Longfei Li, Zhibo Wang, Zhan Qin, Kui Ren
Natural Language Task Steering Vector Unbiased Representation Explainable Causal Semantic Bias

September 23, 2023

Towards LLM-guided Causal Explainability for Black-box Text Classifiers
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, Huan Liu
Natural Language Processing Inherent Interpretability High Explainability Black Box Counterfactual Explanation Explainable Causal

April 8, 2023

Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts
Muskan Garg
Mental Health Social Medium Data Multi Class Classification Reason Giving Causal Analysis Long Text Causal Categorization Explainable Causal

November 18, 2022

Identifying Unique Causal Network from Nonstationary Time Series
Mingyu Kang, Duxin Chen, Ning Meng, Gang Yan, Wenwu Yu
Time Series Causal Network Causal Entropy Explainable Causal

October 16, 2022

Explainable Causal Analysis of Mental Health on Social Media Data
Chandni Saxena, Muskan Garg, Gunjan Ansari
Mental Health Social Medium Data Multi Class Classification Causal Explanation Causal Categorization Explainable Causal

August 29, 2022

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, Xiaohui Hu
Relation Extraction Dependency Tree Biomedical Relation Extraction Causal Forest Explainable Causal

May 12, 2022

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, Bing Qin
Causal Reasoning Mobile Health Causal Query Causal Machine Explainable Causal