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Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthcare.
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Jonas Peters, Joris Mooij, Dominik Janzing, and Bernhard Schölkopf · 2012
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Kun Zhang, Zhikun Wang, Jiji Zhang, and Bernhard Schölkopf · 2015
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Hadi Mohaghegh Dolatabadi, Sarah Erfani, and Christopher Leckie · 2020
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Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, and Bryon Aragam · 2020
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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seq2graph: Discovering dynamic dependencies from multivariate time series with multi-level attention
Xuan-Hong Dang, Syed Yousaf Shah, and Petros Zerfos · 2018
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Causal network reconstruction from time series: From theoretical assumptions to practical estimation
Jakob Runge · 2018
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Neural granger causality for nonlinear time series
Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily Fox · 2018
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Conditional density estimation with bayesian normalising flows
Brian L Trippe and Richard E Turner · 2018
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Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
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Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
Jakob Runge · 2020
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The hardness of conditional independence testing and the generalised covariance measure
Rajen D Shah and Jonas Peters · 2020
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Discovering nonlinear relations with minimum predictive information regularization
Tailin Wu, Thomas Breuel, Michael Skuhersky, and Jan Kautz · 2020
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Neural additive vector autoregression models for causal discovery in time series
Bart Bussmann, Jannes Nys, and Steven Latré · 2021
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Ilyes Khemakhem, Ricardo Monti, Robert Leech, and Aapo Hyvarinen · 2021
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Causal inference for time series analysis: Problems, methods and evaluation
Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, and Huan Liu · 2021
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Vicause: Simultaneous missing value imputation and causal discovery with groups
Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, and Cheng Zhang · 2021
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Survey and evaluation of causal discovery methods for time series
Charles K Assaad, Emilie Devijver, and Eric Gaussier · 2022
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Deep end-to-end causal inference
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Amortized causal discovery: Learning to infer causal graphs from time-series data
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