Neural Causal Model

Neural causal models aim to integrate causal reasoning into machine learning, moving beyond simple correlation to understand cause-and-effect relationships within data. Current research focuses on developing and improving neural network architectures, such as those based on structural causal models and generative adversarial networks, to learn and represent causal structures, enabling tasks like counterfactual inference and robust generalization. This work is significant for improving the reliability and interpretability of machine learning models, particularly in domains with limited data or complex interactions, leading to more trustworthy and effective applications in diverse fields.

Papers

July 12, 2024

Graph Neural Network Causal Explanation via Neural Causal Models
Arman Behnam, Binghui Wang
Graph Neural Network Causal Structure Structural Causal Model Causal Explanation Neural Causal Model GNN Explainers

May 24, 2024

Consistency of Neural Causal Partial Identification
Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis
Causal Graph Strong Consistency Structural Causal Model Neural Causal Model

May 22, 2024

A theory of neural emulators
Catalin C. Mitelut
Neural Network Theoretical Understanding Brain Activity Neural Dynamic Brain Computation Neural Causal Model

April 1, 2024

TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
Omar Faruque, Sahara Ali, Xue Zheng, Jianwu Wang
Time Series Causal Relation Causal Discovery Method Neural Causal Model Causal Dynamic Learning

February 27, 2024

Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response
Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox
Application Proficiency Differential Equation Hybrid Model Mechanistic Model Glucose Control Neural Causal Model

February 7, 2024

Counterfactual Image Editing
Yushu Pan, Elias Bareinboim
Counterfactual Image Neural Causal Model Counterfactual Editing

January 5, 2024

Neural Causal Abstractions
Kevin Xia, Elias Bareinboim
Causal Inference Causal Abstraction Causal Inference Task Neural Causal Model

October 30, 2023

A General Neural Causal Model for Interactive Recommendation
Jialin Liu, Xinyan Su, Peng Zhou, Xiangyu Zhao, Jun Li
Personalized Recommendation Counterfactual Inference Structural Causal Neural Causal Model

August 21, 2023

Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models
Preben M. Ness, Dusica Marijan, Sunanda Bose
Adversarial Attack Adversarial Robustness Mixed Effect Neural Network Model Causal Disentanglement Neural Causal Model Disentanglement Metric

March 22, 2023

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang
Speech Presence Causal Reasoning Latent Confounders ADMM Algorithm Neural Causal Model Autoregressive Flow

January 30, 2023

Causality-based CTR Prediction using Graph Neural Networks
Panyu Zhai, Yanwu Yang, Chunjie Zhang
Graph Neural Network CTR Prediction Neural Causal Model

January 19, 2023

Spatio-temporal neural structural causal models for bike flow prediction
Pan Deng, Yu Zhao, Junting Liu, Xiaofeng Jia, Mulan Wang
Causal Graph Traffic Prediction Temporal Correlation Neural Causal Model

October 24, 2022

Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Luca Galimberti, Anastasis Kratsios, Giulia Livieri
Neural Network Case Relevance Neural Causal Model Deep Causal Causal Objective Function

September 30, 2022

Neural Causal Models for Counterfactual Identification and Estimation
Kevin Xia, Yushu Pan, Elias Bareinboim
Estimation Task Counterfactual Reasoning Counterfactual Distribution Neural Causal Model Counterfactual State

July 4, 2022

De-Biasing Generative Models using Counterfactual Methods
Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi, Gregory Pottie
Generative Model Causal Learning Counterfactual Method Neural Causal Model Causal Generative Counterfactual Generative

June 9, 2022

On the Generalization and Adaption Performance of Causal Models
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke
Strong Generalization Causal Structure Neural Causal Model Differentiable Causal Discovery AdaPtation Performance Better Out of Distribution Generalization