Causal Generative

Causal generative modeling aims to create artificial data that accurately reflects real-world causal relationships, enabling counterfactual reasoning and improved understanding of complex systems. Current research focuses on developing deep generative models, such as variational autoencoders and generative adversarial networks, that incorporate structural causal models to learn and represent these relationships, often employing techniques like counterfactual inference and disentanglement of latent variables. This field is significant for its potential to enhance interpretability in machine learning, improve the robustness and fairness of AI systems, and facilitate causal inference in diverse scientific domains, including medicine and robotics.

Papers

October 8, 2024

Zero-Shot Learning of Causal Models
Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon
Generative Model Causal Structure Structural Causal Model Generative Process Causal Generative

September 9, 2024

CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
Seungheun Baek, Soyon Park, Yan Ting Chok, Junhyun Lee, Jueon Park, Mogan Gim, Jaewoo Kang
Single Cell Lung VAE VAE Model Causal Generative Artifact Disentanglement Network Genetic Perturbation

May 25, 2024

Deep Causal Generative Models with Property Control
Qilong Zhao, Shiyu Wang, Guangji Bai, Bo Pan, Zhaohui Qin, Liang Zhao
Generative Model Variational Autoencoder Causal Graph Latent Variable Causal Generative

April 10, 2024

FiP: a Fixed-Point Approach for Causal Generative Modeling
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
Causal Structure Structural Causal Model Two Stage Fixed Point Causal Order Causal Generative

March 27, 2024

Semi-Supervised Learning for Deep Causal Generative Models
Yasin Ibrahim, Hermione Warr, Konstantinos Kamnitsas
Generative Model Semi Supervised Learning Causal Inference Causal Generative Causal Deep Learning

March 21, 2024

A Causal Bayesian Network and Probabilistic Programming Based Reasoning Framework for Robot Manipulation Under Uncertainty
Ricardo Cannizzaro, Michael Groom, Jonathan Routley, Robert Osazuwa Ness, Lars Kunze
Real World Manipulation Task Causal Reasoning Action Selection Causal Generative

January 21, 2024

Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
Will Taylor-Melanson, Zahra Sadeghi, Stan Matwin
Case Study Counterfactual Explanation MNIST Dataset Counterfactual Inference Counterfactual Image Causal Generative Counterfactual Generative Generative Explanation

January 2, 2024

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
Md Musfiqur Rahman, Murat Kocaoglu
Causal Structure Causal Query Causal Generative Deep Causal Modular Learning Causal Hierarchy High Dimensional Causal

October 17, 2023

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
Timely Survey Causal Model Structural Causal Model Causal Representation Learning Causal Representation Counterfactual Generation Causal Generative

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 30, 2022

Causal Machine Learning: A Survey and Open Problems
Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva
Timely Survey Open Problem Causal Machine Learning Causal Explanation Causal Machine Causal Reinforcement Learning Causal Generative

May 21, 2022

Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng, Deli Zhao, Jingren Zhou, Qibin Sun, Zheng-Jun Zha
Generative Model GAN Model Causal Generative Knowledge Extrapolation

April 12, 2022

What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, Colin Raffel
Zero Shot Transformer Language Model Decoder Model Causal Generative Winner Determination

December 10, 2021

On Causally Disentangled Representations
Abbavaram Gowtham Reddy, Benin Godfrey L, Vineeth N Balasubramanian
Generative Process Causal Disentanglement Generative Factor Causal Generative Concept Disentanglement Disentangled Causal