CAnDOIT: Causal Discovery with Observational and Interventional Data
from Time-Series

v1v2v3 (latest)

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series

3 October 2024

Nicola Bellotto

ArXiv (abs)PDF HTML Github (32★)

Papers citing "CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series"

8 / 8 papers shown

Title
Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments Luca Castri Gloria Beraldo Nicola Bellotto 90 0 0 16 Apr 2025
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios Luca Castri Sariah Mghames Marc Hanheide Nicola Bellotto CML 64 13 0 20 Feb 2023
Learning Temporally Causal Latent Processes from General Temporal Data Weiran Yao Yuewen Sun Alex Ho Changyin Sun Kun Zhang BDL CML 93 87 0 11 Oct 2021
Efficient Neural Causal Discovery without Acyclicity Constraints Phillip Lippe Taco S. Cohen E. Gavves CML 73 72 0 22 Jul 2021
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA Sébastien Lachapelle Pau Rodríguez López Yash Sharma Katie Everett Rémi Le Priol Alexandre Lacoste Simon Lacoste-Julien CML OOD 102 141 0 21 Jul 2021
High-recall causal discovery for autocorrelated time series with latent confounders Andreas Gerhardus J. Runge CML AI4TS 86 102 0 03 Jul 2020
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets Jakob Runge 86 194 0 07 Mar 2020
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 121 871 0 18 Jan 2019

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.