Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data

16 April 2021

Papers citing "Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data"

8 / 8 papers shown

Title
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 49 0 0 21 Mar 2025
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes Georg Manten Cecilia Casolo E. Ferrucci Søren Wengel Mogensen C. Salvi Niki Kilbertus CML BDL 34 8 0 28 Feb 2024
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data Uzma Hasan Emam Hossain Md. Osman Gani CML AI4TS 26 24 0 27 Mar 2023
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network Yang Sun Yifan Xie BDL CML 21 1 0 28 Jan 2023
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery Martina Cinquini F. Giannotti Riccardo Guidotti 11 10 0 18 Jan 2023
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 17 65 0 12 Apr 2021
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric P. Xing CML 103 258 0 29 Sep 2019
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty Ali Shojaie George Michailidis CML 68 214 0 03 Jul 2010