Novel Causal Model

Novel causal models aim to improve the accuracy, interpretability, and robustness of machine learning by explicitly incorporating causal relationships within data. Current research focuses on developing methods for causal discovery and inference in various data types (e.g., time series, categorical data, graph-structured data), often leveraging techniques like double machine learning, Granger causality (especially in extremes), and structural causal models. These advancements are significant for enhancing the reliability of AI systems across diverse applications, from improving explainability in AI (XAI) to enabling more accurate predictions and decision-making in domains like robotics, climate science, and finance.

Papers

November 12, 2024

Language Models as Causal Effect Generators
Lucius E.J. Bynum, Kyunghyun Cho
Language Model Causal Inference Causal Structure Causal Model Structural Causal Model Novel Causal Model

October 16, 2024

Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making
Stelios Triantafyllou, Aleksa Sukovic, Yasaman Zolfimoselo, Goran Radanovic
Counterfactual Prediction Counterfactual Method Multi Agent Sequential Decision Novel Causal Model

July 26, 2024

Understanding XAI Through the Philosopher's Lens: A Historical Perspective
Martina Mattioli, Antonio Emanuele Cinà, Marcello Pelillo
Artificial Intelligence Explainable AI Line by Line Explanation Camera Lens xAI Community Historical Text Epistemic Space Novel Causal Model

July 12, 2024

Granger Causality in Extremes
Juraj Bodik, Olivier C. Pasche
Causal Pattern Granger Causality Extreme Value Novel Causal Model Structural Causal Game

February 20, 2024

Causal hybrid modeling with double machine learning
Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Causal Inference Hybrid Model Heterogeneous Treatment Effect Hybrid Modeling Double Machine Learning Novel Causal Model

January 31, 2024

CauESC: A Causal Aware Model for Emotional Support Conversation
Wei Chen, Hengxu Lin, Qun Zhang, Xiaojin Zhang, Xiang Bai, Xuanjing Huang, Zhongyu Wei
Underlying Emotion Fine Grained Emotion Novel Causal Model Emotional Support Conversation Emotion Cause

December 21, 2023

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao
Adversarial Example Novel Causal Model Counterfactual Generative Generating Process

December 15, 2023

Rethinking Causal Relationships Learning in Graph Neural Networks
Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si, Yifan Jin, Fengge Wu, Changwen Zheng, Huaping Liu
Graph Neural Network Causal Relationship Causal Learning Novel Causal Model Causal Categorization

October 23, 2023

Efficient Causal Discovery for Robotics Applications
Luca Castri, Sariah Mghames, Nicola Bellotto
Real World Human Robot Interaction Causal Discovery Causal Model Causal Analysis Robotics Application Novel Causal Model

September 23, 2023

Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Alan Q. Wang, Minh Nguyen, Mert R. Sabuncu
Human Head Unseen Domain Invariant Representation Invariant Feature Novel Causal Model Nonparametric Modeling Domain Generalization Datasets

September 19, 2023

Partially Specified Causal Simulations
A. Zamanian, L. Mareis, N. Ahmidi
Causal Inference Novel Causal Model Causal Graphical Model

August 19, 2023

Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Ricardo Cannizzaro, Rhys Howard, Paulina Lewinska, Lars Kunze
Line by Line Explanation Scientific Inference Causal Model Causal Pattern Autonomous Drone Novel Causal Model Bayesian Causal Discovery

February 17, 2023

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley
Motion Forecasting Novel Causal Model

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

August 12, 2022

Probabilistic Variational Causal Approach in Observational Studies
Usef Faghihi, Amir Saki
Causal Effect Causal Relation Causal Analysis Causal Reasoning Novel Causal Model Variational Causal

May 5, 2022

Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks
Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh
Physic Informed Neural Network Soft Constraint Linear Convection Failure Mode Novel Causal Model Lagrangian Neural Network Convection Diffusion Equation

March 16, 2022

Counterfactual Inference of Second Opinions
Nina L. Corvelo Benz, Manuel Gomez Rodriguez
Structural Causal Model Causal Direction Counterfactual Inference Novel Causal Model

February 14, 2022

Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael Bronstein, Marcello Restelli
Markov Decision Process Model Based Reinforcement Learning Sequential Decision Systematic Generalization Reward Free Novel Causal Model

January 5, 2022

CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah
Causal Model Novel Causal Model