Causal Constraint

Causal constraint research focuses on incorporating causal relationships into machine learning models to improve fairness, interpretability, and robustness. Current efforts center on developing algorithms that integrate causal knowledge, such as through path-specific effects or causal graphs, into model training and counterfactual explanation generation, often employing techniques like optimal transport, variational autoencoders, and reinforcement learning. This work is significant because it addresses limitations of traditional machine learning approaches by enhancing model transparency, mitigating biases stemming from confounding variables, and improving the reliability of predictions and decisions in high-stakes applications. The resulting methods are increasingly used to generate more actionable and fairer predictions across various domains.

Papers

October 18, 2024

CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers and Fully-Connected Neural Networks for Causally Constrained Predictions
Matthew J. Vowels, Mathieu Rochat, Sina Akbari
Transformer Megatron Decepticons Causal Structure Causal Direction Directed Acyclic Graph Causal Transformer Fully Connected Neural Network Fully Connected Causal Constraint High Reward DAG

August 3, 2024

Fair Risk Minimization under Causal Path-Specific Effect Constraints
Razieh Nabi, David Benkeser
Empirical Risk Minimization Algorithmic Fairness Constrained Optimization Fair Predictor Causal Constraint Constrained Risk

May 28, 2024

Counterfactual Explanations for Multivariate Time-Series without Training Datasets
Xiangyu Sun, Raquel Aoki, Kevin H. Wilson
Machine Learning Training Data Counterfactual Explanation Multivariate Time Series Opaque Machine Learning Causal Constraint

April 20, 2024

A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density
Kleopatra Markou, Dimitrios Tomaras, Vana Kalogeraki, Dimitrios Gunopulos
New Framework Variational Autoencoder Sparsity Increase Causal Pattern Particle Density Causal Constraint Counterfactual Sequence

April 18, 2024

Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
Roger Pros, Jordi Vitrià
Neural Network Causal Graph Novel Approach Treatment Effect Algorithmic Bias Causal Constraint

April 5, 2024

Bounding Causal Effects with Leaky Instruments
David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva
Causal Effect Instrumental Variable Audio Effect Inference Strategy Causal Constraint

February 4, 2024

Can Large Language Models Learn Independent Causal Mechanisms?
Gaël Gendron, Bao Trung Nguyen, Alex Yuxuan Peng, Michael Witbrock, Gillian Dobbie
Large Language Model Causal Model Language Modelling Causal Variable Causal Constraint

December 20, 2023

Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach
Wen Huang, Xintao Wu
Bandit Algorithm Regret Analysis Causal Approach Systematic Bias Causal Constraint Offline Dataset Contextual Bandit Setting

December 13, 2023

Causal Optimal Transport of Abstractions
Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas
Optimal Transport Structural Causal Model Metric Aware Abstraction Causal Abstraction Causal Constraint

November 13, 2023

Towards Bounding Causal Effects under Markov Equivalence
Alexis Bellot
Causal Effect Markov Equivalence Class Causal Constraint

August 7, 2023

Provably Efficient Learning in Partially Observable Contextual Bandit
Xueping Gong, Jiheng Zhang
Efficient Learning Bandit Algorithm Causal Constraint

August 4, 2023

Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar
Causal Inference Causal Graph Causal Query Scalable Algorithm Causal Constraint

July 25, 2023

Fundamental causal bounds of quantum random access memories
Yunfei Wang, Yuri Alexeev, Liang Jiang, Frederic T. Chong, Junyu Liu
Quantum Physic Quantum Device Random Access Memory Causal Constraint Quantum Memory Quantum Material

December 5, 2022

Observational and Interventional Causal Learning for Regret-Minimizing Control
Christian Reiser
External Control Causal Discovery Structural Causal Model Causal Representation Causal Constraint Observational Causal

October 12, 2022

Feasible and Desirable Counterfactual Generation by Preserving Human Defined Constraints
Homayun Afrabandpey, Michael Spranger
Counterfactual Generation Causal Constraint Feasible Task Global Constraint Feasibility Constraint

August 19, 2022

Disentangled Representation with Causal Constraints for Counterfactual Fairness
Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang
Group Fairness Disentangled Representation Fair Representation Counterfactual Fairness Causal Constraint