Causal Deep Learning

Causal deep learning aims to integrate causal inference principles into deep learning models, enabling the prediction of "what if" scenarios and a deeper understanding of cause-and-effect relationships within complex data. Current research focuses on developing novel architectures, such as deepsets and neural stochastic differential equations, to address challenges like spatio-temporal interference and incomplete data, often leveraging techniques like do-calculus and semi-supervised learning. This field is significant for improving the robustness and generalizability of deep learning models across various domains, including healthcare, economics, and software engineering, by moving beyond mere correlation to uncover true causal mechanisms.

Papers

July 25, 2024

Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences
Runpeng Dai, Jianing Wang, Fan Zhou, Shikai Luo, Zhiwei Qin, Chengchun Shi, Hongtu Zhu
Mean Field Policy Evaluation Spatial Domain Causal Deep Learning Spatial Interference

June 18, 2024

Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs
Joshua Durso-Finley, Berardino Barile, Jean-Pierre Falet, Douglas L. Arnold, Nick Pawlowski, Tal Arbel
Causal Inference Treatment Effect Temporal Pattern Disease Progression Neural SDEs Disease Trajectory Counterfactual Path Causal Deep Learning

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

February 20, 2024

Quantitative causality, causality-guided scientific discovery, and causal machine learning
X. San Liang, Dake Chen, Renhe Zhang
Causal Pattern Earth System Causal Machine Learning Causality Analysis Causal Deep Learning Decadal Temperature Prediction

January 16, 2024

DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
Jiaming Tang
Causal Effect Causal Representation Estimation Performance Click Model Deep Causal Causal Deep Learning Multi Touch Attribution

October 12, 2023

Towards Causal Deep Learning for Vulnerability Detection
Md Mahbubur Rahman, Ira Ceka, Chengzhi Mao, Saikat Chakraborty, Baishakhi Ray, Wei Le
Native Robustness Vulnerability Detection Causal Learning Causal Approach Causal Deep Learning Causal Learning Algorithm

March 3, 2023

Causal Deep Learning
Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar
Causal Pattern Causal Knowledge Causal Deep Learning

January 1, 2023

Causal Deep Learning: Causal Capsules and Tensor Transformers
M. Alex O. Vasilescu
Causal Machine Tensor Model Causal Deep Learning Factor Tensor Patch Tensor

December 23, 2022

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

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