Interpretable Models for Granger Causality Using Self-explaining Neural Networks

19 January 2021

Papers citing "Interpretable Models for Granger Causality Using Self-explaining Neural Networks"

8 / 8 papers shown

Title
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning Albert Wilcox Mohamed Ghanem Masoud Moghani Pierre Barroso Benjamin Joffe Animesh Garg 50 0 0 06 Mar 2025
CausalMMM: Learning Causal Structure for Marketing Mix Modeling Chang Gong Di Yao Lei Zhang Sheng Chen Wenbin Li Yueyang Su Jingping Bi 34 4 0 24 Jun 2024
Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise Augusto Santos Diogo Rente Rui Seabra José M. F. Moura 11 4 0 10 Dec 2023
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data Uzma Hasan Emam Hossain Md. Osman Gani CML AI4TS 31 24 0 27 Mar 2023
Causal Recurrent Variational Autoencoder for Medical Time Series Generation Hongming Li Shujian Yu José C. Príncipe CML BDL MedIm 28 47 0 16 Jan 2023
Deep Causal Learning: Representation, Discovery and Inference Zizhen Deng Xiaolong Zheng Hu Tian D. Zeng CML BDL 36 11 0 07 Nov 2022
Bayesian Spillover Graphs for Dynamic Networks Grace Deng David S. Matteson 14 3 0 03 Mar 2022
Neural graphical modelling in continuous-time: consistency guarantees and algorithms Alexis Bellot K. Branson M. Schaar CML AI4TS 24 43 0 06 May 2021