Data Driven Causal

Data-driven causal discovery aims to automatically infer causal relationships from observational data, overcoming limitations of traditional expert-driven approaches. Current research heavily utilizes large language models (LLMs) to augment or replace human expertise in constructing causal graphs, often integrating them with constraint-based algorithms or neural networks for improved accuracy and scalability. This field is crucial for advancing scientific understanding across diverse domains, from complex systems analysis to industrial process optimization, by enabling more robust and reliable causal inference from increasingly available data.

Papers

November 18, 2024

Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation
Hechuan Wen, Tong Chen, Guanhua Ye, Li Kheng Chai, Shazia Sadiq, Hongzhi Yin
Causal Effect Estimation Data Driven Causal Treatment Outcome Prediction Counterfactual Sample Active Causal Effect Estimation

October 22, 2024

Improving Causal Reasoning in Large Language Models: A Survey
Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Qingzhen Liu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
Language Model Timely Survey Complex Reasoning Causal Pattern Causal Reasoning Data Driven Causal

July 11, 2024

Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight
Zhiqiang Xie, Yujia Zheng, Lizi Ottens, Kun Zhang, Christos Kozyrakis, Jonathan Mace
Language Model Causal Graph Performance Metric Cloud Service Atlas Reliability Map Fault Localization Data Driven Causal Causal Insight

June 11, 2024

Large Language Models for Constrained-Based Causal Discovery
Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz, Vasileios Sitokonstantinou, Gustau Camps-Valls
Causal Graph Data Driven Causal Constraint Based Causal Discovery

May 7, 2024

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan
Heterogeneous Environment Causal Categorization Invariance Learning Data Driven Causal Adversarial Estimator

May 2, 2024

ALCM: Autonomous LLM-Augmented Causal Discovery Framework
Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, Amir M. Rahmani
Causal Discovery Legal Autonomy Causal Structure Learning Data Driven Causal

April 3, 2024

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
Sahara Ali, Uzma Hasan, Xingyan Li, Omar Faruque, Akila Sampath, Yiyi Huang, Md Osman Gani, Jianwu Wang
Time Series Narrative Review Causal Discovery Causal Pattern Data Driven Causal Temporal Spatial Causal

February 28, 2024

Cause and Effect: Can Large Language Models Truly Understand Causality?
Swagata Ashwani, Kshiteesh Hegde, Nishith Reddy Mannuru, Mayank Jindal, Dushyant Singh Sengar, Krishna Chaitanya Rao Kathala, Dishant Banga, Vinija Jain, Aman Chadha
Mixed Effect Causal Pattern Actual Cause Causal Reasoning Event Causality Identification Causal Machine Data Driven Causal

June 29, 2023

From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, Huanhuan Chen
Raw Data Causal Discovery Causal Structure Learning Data Driven Causal

May 9, 2023

Causal Discovery from Subsampled Time Series with Proxy Variables
Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang
Causal Discovery Causal Structure Subsampling Method Causal Discovery Algorithm Unobserved Node Data Driven Causal

October 13, 2022

On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
Estimation Task Causal Direction Novel Identifiability Test Consistent Estimator Heteroscedastic Noise Data Driven Causal

September 20, 2022

On a Uniform Causality Model for Industrial Automation
Maria Krantz, Alexander Windmann, Rene Heesch, Lukas Moddemann, Oliver Niggemann
Causal Pattern Cyber Physical System Causal Relation Industrial Control Data Driven Causal

August 2, 2022

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
Giovanni Menegozzo, Diego Dall'Alba, Paolo Fiorini
Causal Relationship Fault Detection Manufacturing Process Data Driven Causal

June 21, 2022

Using cognitive psychology to understand GPT-3
Marcel Binz, Eric Schulz
Multi Armed Bandit Model Based Reinforcement Learning Causal Reasoning GPT 3 Directed Exploration Data Driven Causal Cognitive Psychology