Cause Effect

Cause-effect inference aims to uncover the true causal relationships between variables, moving beyond simple correlations. Current research focuses on developing robust algorithms for causal discovery, particularly in high-dimensional and complex systems, employing techniques like constraint-based methods, score-based methods, and those leveraging large language models and structural equation models to analyze diverse data types (including text and time series). These advancements are crucial for improving the explainability and trustworthiness of AI systems, enhancing scientific understanding in various fields (e.g., medicine, climate science), and enabling more effective decision-making in complex real-world scenarios.

Papers

June 16, 2023

Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation
Sofia Gil-Clavel, Tatiana Filatova
Natural Language Processing Application Proficiency Network Programming Text Summarization Systematic Literature Review Literature Review Text Analysis Cause Effect

May 5, 2023

Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu, Kiattikun Chobtham, Arian Hashemzadeh, Praharsh A. Nanavati, Rendani Mbuvha, Bruno Petrungaro
Case Study Covid 19 Causal Representation Open Problem Cause Effect Causal Structure Learning Causal Machine Learning United Kingdom

April 23, 2023

Meaningful Causal Aggregation and Paradoxical Confounding
Yuchen Zhu, Kailash Budhathoki, Jonas Kuebler, Dominik Janzing
Causal Pattern Causal Relation Cause Effect Causal System Non Confounding Covariates Q Aggregation

March 27, 2023

A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
Uzma Hasan, Emam Hossain, Md Osman Gani
Timely Survey Causal Discovery Time Series Data Causal Relation Causal Discovery Method Cause Effect Causal Discovery Algorithm

February 14, 2023

A Review of the Role of Causality in Developing Trustworthy AI Systems
Niloy Ganguly, Dren Fazlija, Maryam Badar, Marco Fisichella, Sandipan Sikdar, Johanna Schrader, Jonas Wallat, Koustav Rudra, Manolis Koubarakis, Gourab K. Patro, Wadhah Zai El Amri, Wolfgang Nejdl
Integral Role Causal Model Causal Pattern Trustworthy Artificial Intelligence Cause Effect Causal Approach

February 11, 2023

Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Elisabeth Ailer, Jason Hartford, Niki Kilbertus
High Dimensional Causal Effect Instrumental Variable Cause Effect Treatment Variable

January 26, 2023

Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Xiangyu Sun, Oliver Schulte
Feature Selection Causal Discovery Maximum Likelihood Causal Direction Cause Effect Independence Testing

January 14, 2023

Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, W. Dhammika Widanage, Theodoros Damoulas
Structural Causal Model Cognitive Abstraction Cause Effect Metric Aware Abstraction Causal Abstraction Interventional Distribution

January 6, 2023

Causal Categorization of Mental Health Posts using Transformers
Simranjeet Kaur, Ritika Bhardwaj, Aastha Jain, Muskan Garg, Chandni Saxena
Transformer Megatron Decepticons Mental Health Cause Effect Causal Categorization

December 23, 2022

On How AI Needs to Change to Advance the Science of Drug Discovery
Kieran Didi, Matej Zečević
Artificial Intelligence Causal Discovery Drug Discovery Causal Pattern Science Journalism Cause Effect

November 10, 2022

Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues, John Crawford, Emre Kiciman, Ranveer Chandra
Causal Model Improved Generalization Cause Effect Soil Process Carbon Estimation

November 8, 2022

From Causal Pairs to Causal Graphs
Rezaur Rashid, Jawad Chowdhury, Gabriel Terejanu
Causal Graph Causal Structure Cause Effect Probabilistic Information

October 26, 2022

Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Maximilian Kertel, Stefan Harmeling, Markus Pauly
Cause Effect Structural Equation Structural Causal Process Control

September 18, 2022

Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Yang Ni
Classification Code Causal Discovery Categorical Data Cause Effect Causal Discovery Algorithm Novel Causal Model

September 12, 2022

A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks
Maximilian Diehl, Karinne Ramirez-Amaro
Robotic Task Cause Effect Causal Approach Causal Bayesian Network Silent Failure

July 30, 2022

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data
Jackie Ayoub, Zifei Wang, Meitang Li, Huizhong Guo, Rini Sherony, Shan Bao, Feng Zhou
Literature Review Driver Assistance System Systematic Comparison Cause Effect Consumer Complaint Ada Case Study

June 16, 2022

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data
Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
Causal Discovery Limited Data Causal Discovery Method Cause Effect Empirical Bayes Constraint Based Causal Discovery Frequentist Inference

May 10, 2022

On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Domain Adaptation Semi Supervised Learning Unsupervised Domain Adaptation Causal Pattern Causal Learning Cause Effect Causal Prediction

May 5, 2022

Action Languages Based Actual Causality for Computational Ethics: a Sound and Complete Implementation in ASP
Camilo Sarmiento, Gauvain Bourgne, Katsumi Inoue, Daniele Cavalli, Jean-Gabriel Ganascia
Causal Pattern Logic Programming Cause Effect Implementation Detail Actual Causality Machine Ethic Language Action

April 21, 2022

Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems
Christophe Bédard, Pierre-Yves Lajoie, Giovanni Beltrame, Michel Dagenais
System Description Robot Operating System Cause Effect Computation Graph Aware Job Message Flow

Cause Effect

Papers

Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation

Open problems in causal structure learning: A case study of COVID-19 in the UK

Meaningful Causal Aggregation and Paradoxical Confounding

A Survey on Causal Discovery Methods for I.I.D. and Time Series Data

A Review of the Role of Causality in Developing Trustworthy AI Systems

Sequential Underspecified Instrument Selection for Cause-Effect Estimation

Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing

Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions

Causal Categorization of Mental Health Posts using Transformers

On How AI Needs to Change to Advance the Science of Drug Discovery

Causal Modeling of Soil Processes for Improved Generalization

From Causal Pairs to Causal Graphs

Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data

On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models

Action Languages Based Actual Causality for Computational Ethics: a Sound and Complete Implementation in ASP

Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems