Deep Causal

Deep causal learning integrates deep neural networks with causal inference methods to address challenges in uncovering and quantifying causal relationships within complex data. Current research focuses on developing deep learning architectures for causal discovery and inference, particularly in high-dimensional settings like images and time series data, often employing techniques like counterfactual generation and generative models. This field is significant for its potential to improve the accuracy and interpretability of causal analyses across diverse domains, ranging from economic forecasting and policy evaluation to personalized medicine and robotics. The development of robust and efficient deep causal models promises to enhance our understanding of complex systems and facilitate more effective decision-making.

Papers

August 4, 2024

A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks
Zhe Yu, Chi Xia, Shaosheng Cao, Lin Zhou
Propensity Score Uplift Modeling Price Elasticity Ride Hailing Deep Causal Ride Hailing Service

July 15, 2024

Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
Philipp Kai Peter, Yulin Li, Ziyue Li, Wolfgang Ketter
Energy Policy Research Deep Causal Gas Price Neural Granger Geo Event

May 2, 2024

A deep causal inference model for fully-interpretable travel behaviour analysis
Kimia Kamal, Bilal Farooq
Causal Inference Travel Mode Causal Inference Task Travel Behavior Deep Causal

January 16, 2024

January 2, 2024

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

November 2, 2023

A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations
Hang Chen, Keqing Du, Chenguang Li, Xinyu Yang
Meaningful Representation Causal Structure Causal Relationship Requirement Based Roadmap Deep Causal Causal Concept

June 27, 2023

High Fidelity Image Counterfactuals with Probabilistic Causal Models
Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker
Causal Model Causal Mediation Analysis Deep Causal Deep Structural Causal

December 23, 2022

Deep Causal Learning for Robotic Intelligence
Yangming Li
Causal Discovery Causal Learning Intelligent Robot Deep Causal Causal Deep Learning

November 7, 2022

Deep Causal Learning: Representation, Discovery and Inference
Zizhen Deng, Xiaolong Zheng, Hu Tian, Daniel Dajun Zeng
Scientific Inference Scientific Discovery Individual Representation Causal Learning Causal Assumption Deep Causal

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 19, 2022

A Survey of Deep Causal Models and Their Industrial Applications
Zongyu Li, Xiaobo Guo, Siwei Qiang
Timely Survey Causal Effect Causal Model Causal Effect Estimation Industrial Application Counterfactual Data Deep Causal

June 21, 2022

Interpretable Deep Causal Learning for Moderation Effects
Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
Conditional Average Treatment Effect Causal Machine Scoring Function Deep Causal Counterfactual Recurrent Network

January 31, 2022

Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer
Beatrice Acciaio, Anastasis Kratsios, Gudmund Pammer
Universal Approximation Statistical Manifold Geometric Structure Deep Causal Causal Deep Learning