Causal Feature

Causal feature research aims to identify the variables truly driving an outcome, disentangling them from spurious correlations. Current work focuses on developing methods to discover these features within various data modalities (text, images, graphs) using techniques like causal discovery algorithms, generative models, and contrastive learning, often incorporating meta-learning or backdoor adjustment strategies. This research is crucial for improving the robustness, generalizability, and interpretability of machine learning models across diverse domains, impacting fields ranging from healthcare and climate science to natural language processing.

Papers

November 1, 2024

Towards Robust Text Classification: Mitigating Spurious Correlations with Causal Learning
Yuqing Zhou, Ziwei Zhu
Feature Selection Spurious Correlation Causal Learning Causal Feature Robust Classifier Inverse Propensity Weighting Robust Text Classification

October 29, 2024

CausAdv: A Causal-based Framework for Detecting Adversarial Examples
Hichem Debbi
Adversarial Example Adversarial Robustness Adversarial Perturbation Adversarial Text Causal Feature Causal Approach

October 16, 2024

October 8, 2024

Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Wei Liu, Zhiying Deng, Zhongyu Niu, Jun Wang, Haozhao Wang, YuanKai Zhang, Ruixuan Li
Inherent Interpretability High Explainability Mutual Information Causal Reasoning Causal Feature Self Rationalization Human Annotated Rationale Rationale Extraction

September 20, 2024

Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network
Haoran Li, Qiang Gao, Hongmei Wu, Li Huang
Causal Relation Causal Feature Event Causality Identification Semantic Dependency

August 29, 2024

Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
Boyu Chen, Junjie Liu, Zhu Li, Mengyue yang
Multimodal Representation Modality Specific Causal Representation Multimodal Representation Learning Causal Feature

August 26, 2024

PAGE: Parametric Generative Explainer for Graph Neural Network
Yang Qiu, Wei Liu, Jun Wang, Ruixuan Li
Graph Neural Network Latent Causal Causal Feature Web Page Structured Explanation Generative Explanation

March 13, 2024

A Causal Inspired Early-Branching Structure for Domain Generalization
Liang Chen, Yong Zhang, Yibing Song, Zhen Zhang, Lingqiao Liu
Domain Generalization Causal Event Causal Feature Domain Specific Feature Strong Branching

March 6, 2024

Causal Prototype-inspired Contrast Adaptation for Unsupervised Domain Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery
Jingru Zhu, Ya Guo, Geng Sun, Liang Hong, Jie Chen
Contrastive Learning Remote Sensing Semantic Segmentation Model Causal Event Domain Adaptive Semantic Segmentation Causal Feature Invariant Causal Feature Contrast Adaptation

February 29, 2024

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Yang Chen, Yitao Liang, Zhouchen Lin
Imitation Learning Causal Discovery Causal Feature Shot Imitation

February 15, 2024

Do causal predictors generalize better to new domains?
Vivian Y. Nastl, Moritz Hardt
Causal Pattern Causal Feature Causal Prediction Causal Influence New Domain

February 13, 2024

Adjustment Identification Distance: A gadjid for Causal Structure Learning
Leonard Henckel, Theo Würtzen, Sebastian Weichwald
Causal Discovery Causal Structure Learning Causal Discovery Algorithm Causal Feature Causal Ordering

December 15, 2023

Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Ziliang Chen, Yongsen Zheng, Zhao-Rong Lai, Quanlong Guan, Liang Lin
Medical Diagnosis Invariant Representation Causal Feature Causal Approach Invariant Causal Feature

November 21, 2023

Machine-Guided Discovery of a Real-World Rogue Wave Model
Dion Häfner, Johannes Gemmrich, Markus Jochum
Symbolic Regression Causal Feature Data Driven Discovery

November 2, 2023

Invariant Causal Imitation Learning for Generalizable Policies
Ioana Bica, Daniel Jarrett, Mihaela van der Schaar
Causal Feature Policy Generalization Imitation Policy Qualitative Feature Causal Imitation

October 10, 2023

Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization
Caoyun Fan, Wenqing Chen, Jidong Tian, Yitian Li, Hao He, Yaohui Jin
Full Potential Distribution Generalization Causal Feature Unlocking Insight Invariant Causal Feature

September 23, 2023

D-Separation for Causal Self-Explanation
Wei Liu, Jun Wang, Haozhao Wang, Ruixuan Li, Zhiying Deng, YuanKai Zhang, Yang Qiu
Conditional Independence Separation Performance Causal Reasoning Causal Feature Self Explanation Self Explaining Optimal Dependence

August 14, 2023

CausalLM is not optimal for in-context learning
Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, Radu Soricut
Context Learning Causal Language Causal Feature

August 2, 2023

CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models
Robert Maier, Andreas Schlattl, Thomas Guess, Jürgen Mottok
Causal Model Causal Feature Causal Graphical Model Causality Analysis