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"

10 / 10 papers shown

Title
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 52 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 44 8 0 28 Feb 2024
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery Yuxiao Cheng Ziqian Wang Tingxiong Xiao Qin Zhong J. Suo Kunlun He AI4TS CML 30 11 0 03 Oct 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
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network Yang Sun Yifan Xie BDL CML 31 1 0 28 Jan 2023
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery Martina Cinquini F. Giannotti Riccardo Guidotti 18 10 0 18 Jan 2023
CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods Giovanni Menegozzo Diego DallÁlba Paolo Fiorini 20 7 0 02 Aug 2022
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 27 65 0 12 Apr 2021
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric P. Xing CML 111 258 0 29 Sep 2019
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty Ali Shojaie George Michailidis CML 68 214 0 03 Jul 2010