Causal Machine

Causal machine learning (CausalML) aims to move beyond identifying correlations in data to establishing true cause-and-effect relationships, enabling more effective decision-making and intervention planning. Current research focuses on developing and applying methods like double machine learning, causal forests, and Bayesian networks to estimate causal effects in various domains, including supply chain management, healthcare, and industrial troubleshooting. This field is significant because it allows for more robust predictions and counterfactual analysis, leading to improved outcomes in diverse applications ranging from policy evaluation to autonomous robotics.

Papers

September 3, 2024

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand
Visual Analogue Scale Causal Effect Causal Analysis Causal Machine Double Machine Learning

August 24, 2024

What if? Causal Machine Learning in Supply Chain Risk Management
Mateusz Wyrembek, George Baryannis, Alexandra Brintrup
Supply Chain Causal Machine Learning Causal Machine Supply Chain Risk

July 30, 2024

Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept
Alexandre Trilla, Ossee Yiboe, Nenad Mijatovic, Jordi Vitrià
Causal Relationship High Impact Concept Causal Learning Predictive Maintenance Causal Approach Partial Proof Causal Explanation Causal Machine Intelligent Fault Diagnosis

July 15, 2024

The Missing Link: Allocation Performance in Causal Machine Learning
Unai Fischer-Abaigar, Christoph Kern, Frauke Kreuter
Downstream Task Causal Machine Learning Causal Machine Efficient Allocation Missing Link Decision Making Scenario

July 10, 2024

Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
Alexandre Trilla, Rajesh Rajendran, Ossee Yiboe, Quentin Possamaï, Nenad Mijatovic, Jordi Vitrià
Exceptional Point High Impact Concept Root Cause Partial Proof Causal Machine Anomalous Behavior Failure Mode Industrial Time Series

July 8, 2024

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
Luka Kovačević, Izzy Newsham, Sach Mukherjee, John Whittaker
Causal Graph Causal Structure Causal Relationship Structural Causal Model Causal Structure Learning Causal Machine Simulation Benchmark Genetic Perturbation

April 30, 2024

Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment
Lei Wang, Desen Yuan
Adversarial Perturbation Image Quality Assessment Causal Representation Learning Imperceptible Adversarial Perturbation Causal Machine

March 1, 2024

Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning
C. McDonnell, M. Arana-Catania, S. Upadhyay
Reinforcement Learning Robotic Manipulator Causal Machine Learning Planetary Exploration Causal Machine Autonomous Manipulation

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 Causal Reasoning Actual Cause Event Causality Identification Causal Machine Data Driven Causal

October 20, 2023

Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
Patrick Rehill, Nicholas Biddle
Policy Evaluation Transparency Index Causal Machine Learning Important Usability Challenge Causal Machine Causal Forest

October 14, 2023

A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Reinforcement Learning New Framework Causal Analysis Cryptocurrency Price Causal Machine Automated Cryptocurrency Trading Intelligent Trading

October 4, 2023

Multi-Domain Causal Representation Learning via Weak Distributional Invariances
Kartik Ahuja, Amin Mansouri, Yixin Wang
Multi Domain Causal Representation Learning Causal Representation Causal Machine Distributional Invariance

September 2, 2023

Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
Patrick Rehill, Nicholas Biddle
Machine Learning AI Fairness Causal Machine Learning Causal Machine Fairness Implication Heterogeneous Treatment Effect Estimation

June 20, 2023

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Hammaad Adam, Fan Yin, Huibin, Hu, Neil Tenenholtz, Lorin Crawford, Lester Mackey, Allison Koenecke
Heterogeneous Treatment Effect Early Stopping Causal Machine Randomized Experiment Heterogeneous Population

June 7, 2023

Causally Learning an Optimal Rework Policy
Oliver Schacht, Sven Klaassen, Philipp Schwarz, Martin Spindler, Daniel Grünbaum, Sebastian Imhof
Machine Learning Causal Learning Optimal Design Causal Machine Corresponding Yield Stress Value

May 16, 2023

What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem
Jose Alvarez Colmenares
Artificial Intelligence Right Problem Human Decision Maker Biased Decision Causal Machine Fairness Problem

February 1, 2023

A Survey of Methods, Challenges and Perspectives in Causality
Gaël Gendron, Michael Witbrock, Gillian Dobbie
Deep Learning Timely Survey Deep Learning Model Technical Challenge NCD Method Synthesized View Causal Pattern Causal Machine Trend Correlation

January 25, 2023

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum, Weiran Yao, Chenghao Liu, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles
Time Series Causal Inference Causal Discovery Tabular Data Causal Analysis Causal Machine Ground Truth Causal

January 1, 2023

Causal Deep Learning: Causal Capsules and Tensor Transformers
M. Alex O. Vasilescu
Causal Machine Tensor Model Causal Deep Learning Factor Tensor Patch Tensor

July 12, 2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
Decision Making Experimental Design Contextual Information Decision Theory Causal Machine Treatment Assignment Context Optimization Real World Testing

Causal Machine

Papers

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions

What if? Causal Machine Learning in Supply Chain Risk Management

Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept

The Missing Link: Allocation Performance in Causal Machine Learning

Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments

Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment

Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning

Cause and Effect: Can Large Language Models Truly Understand Causality?

Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability

A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading

Multi-Domain Causal Representation Learning via Weak Distributional Invariances

Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations

Causally Learning an Optimal Rework Policy

What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem

A Survey of Methods, Challenges and Perspectives in Causality

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data

Causal Deep Learning: Causal Capsules and Tensor Transformers

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation