Causal Strength

Causal strength quantifies the influence of one variable on another, aiming to move beyond mere correlation to establish genuine cause-and-effect relationships. Current research focuses on developing robust metrics for assessing causal strength across diverse data types and contexts, employing methods like causal graphs, variational autoencoders, and novel algorithms designed to handle confounding variables and complex dependencies. These advancements are improving the reliability of causal inference in various fields, from enhancing the interpretability of AI models to improving the accuracy of soft sensors in industrial processes. The ultimate goal is to build more reliable and trustworthy systems that can accurately model and predict causal effects in complex systems.

Papers

July 6, 2024

Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training
Dingkang Yang, Kun Yang, Haopeng Kuang, Zhaoyu Chen, Yuzheng Wang, Lihua Zhang
Emotion Recognition Causal Strength Causal Intervention Module Context Bias

June 30, 2024

Causality-driven Sequence Segmentation for Enhancing Multiphase Industrial Process Data Analysis and Soft Sensing
Yimeng He, Le Yao, Xinmin Zhang, Xiangyin Kong, Zhihuan Song
Causal Graph Soft Sensor Industrial Process Causal Strength

June 25, 2024

CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems
Tao Feng, Lizhen Qu, Xiaoxi Kang, Gholamreza Haffari
Open Domain Dialogue System Reference Free Metric Causal Strength Causal Score

June 12, 2024

Are Objective Explanatory Evaluation metrics Trustworthy? An Adversarial Analysis
Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegib, Mohamed Deriche
Adversarial Attack Explainable AI XAI Method Evaluation Metric Explainability Method Causal Strength

March 21, 2024

Local Causal Discovery with Linear non-Gaussian Cyclic Models
Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang
Causal Model Linear Ordered Data Causal Strength Independent Low Rank Matrix Analysis Local Causal Discovery

January 19, 2024

Causal Layering via Conditional Entropy
Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese
Causal Discovery Stable Entropy Unobserved Variable Causal Strength Conditional Entropy

January 6, 2024

Exploring Defeasibility in Causal Reasoning
Shaobo Cui, Lazar Milikic, Yiyang Feng, Mete Ismayilzada, Debjit Paul, Antoine Bosselut, Boi Faltings
Causal Reasoning Defeasible Reasoning Causal Strength

January 5, 2024

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, Petar M. Djuric
Interpretable Way Differentiable Model Causal Strength Differentiable Causal Discovery

December 13, 2023

Causality Analysis for Evaluating the Security of Large Language Models
Wei Zhao, Zhe Li, Jun Sun
Adversarial Perturbation Security Related Causality Analysis Causal Strength

November 21, 2023

Causality is all you need
Ning Xu, Yifei Gao, Hongshuo Tian, Yongdong Zhang, An-An Liu
Causal Graph Causal Relationship Causal Pattern Causal Strength

November 13, 2023

In-context Learning and Gradient Descent Revisited
Gilad Deutch, Nadav Magar, Tomer Bar Natan, Guy Dar
Context Learning Gradient Descent Shot Learning Causal Strength Contextual Similarity

October 3, 2023

Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
Causal Inference Causal Effect Estimation Partial Identifiability Variational AutoEncoder Causal Strength Causal Identifiability Front Door Adjustment

May 4, 2023

Towards Causal Representation Learning and Deconfounding from Indefinite Data
Hang Chen, Xinyu Yang, Qing Yang
Causal Discovery Causal Graph Causal Representation Learning Causal Representation Causal Strength

April 24, 2023

Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
Causal Inference Variational Autoencoder Causal Effect Estimation Unobserved Confounders Causal Strength Front Door Criterion

December 18, 2022

A Layered Architecture for Universal Causality
Sridhar Mahadevan
Multi Layer Causal Structure Causal Model Category Theory Categorical Feature Actual Causality Causal Strength