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References (33)

[1]
Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
2025Marcel Wienöbst, Leonard Henckel et al.
[2]
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series
2025Michael Wieck-Sosa, Michel F. C. Haddad et al.
[3]
An Asymmetric Independence Model for Causal Discovery on Path Spaces
2025Georg Manten, Cecilia Casolo et al.
[4]
A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs
2024Tom Hochsprung, Jakob Runge et al.
[5]
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
2023Bryan Andrews, Joseph Ramsey et al.
[6]
Discovering Causal Relations and Equations from Data
2023Gustau Camps-Valls, Andreas Gerhardus et al.
[7]
Structure Learning with Continuous Optimization: A Sober Look and Beyond
2023Ignavier Ng, Biwei Huang et al.
[8]
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
2022Kevin Bello, Bryon Aragam et al.
[9]
Greedy Relaxations of the Sparsest Permutation Algorithm
2022Wai-yin Lam, Bryan Andrews et al.
[10]
Causal Structure Learning: A Combinatorial Perspective
2022C. Squires, Caroline Uhler
[11]
Survey and Evaluation of Causal Discovery Methods for Time Series
2022Charles K. Assaad, Emilie Devijver et al.
[12]
Causal Inference in Time Series
2022M. Xiong
[13]
Characterization of causal ancestral graphs for time series with latent confounders
2021Andreas Gerhardus
[14]
Efficient and Consistent Data-Driven Model Selection for Time Series
2021Jean‐Marc Bardet, Kamila Kare et al.
[15]
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game
2021Alexander G. Reisach, C. Seiler et al.
[16]
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
2020Jakob Runge
[17]
DYNOTEARS: Structure Learning from Time-Series Data
2020Roxana Pamfil, Nisara Sriwattanaworachai et al.
[18]
Inferring causation from time series in Earth system sciences
2019J. Runge, S. Bathiany et al.
[19]
Review of Causal Discovery Methods Based on Graphical Models
2019C. Glymour, Kun Zhang et al.
[20]
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding
2018Daniel Malinsky, P. Spirtes

Showing 20 of 33 references

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