Fetching the paper…
Reading the bibliography…
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
Masked gradient-based causal structure learning
I. Ng, Z. Fang, S. Zhu, Z. Chen, and J. Wang · 1910
Earlier work this paper cites.
A graph autoencoder approach to causal structure learning
I. Ng, S. Zhu, Z. Chen, and Z. Fang · 1911
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
P. Spirtes and C. Glymour · 1991
Earlier work this paper cites.
Causal inference and causal explanation with background knowledge
C. Meek · 1995
Earlier work this paper cites.
Causation, Prediction, and Search
P. Spirtes, C. N. Glymour, and R. Scheines · 2000
Earlier work this paper cites.
Optimal structure identification with greedy search
D. M. Chickering · 2002
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen · 2006
Earlier work this paper cites.
Estimating high-dimensional directed acyclic graphs with the pc-algorithm
M. Kalisch and P. Bühlmann · 2007
Earlier work this paper cites.
On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
J. Zhang · 2008
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
P. O. Hoyer, D. Janzing, J. M. Mooij, J. Peters, and B. Schölkopf · 2009
Earlier work this paper cites.
Directlingam: A direct method for learning a linear non-Gaussian structural equation model
S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P. O. Hoyer, and K. Bollen · 2011
Earlier work this paper cites.
CAM: Causal additive models, high-dimensional order search and penalized regression
P. Bühlmann, J. Peters, and J. Ernest · 2014
Earlier work this paper cites.
Order-independent constraint-based causal structure learning
D. Colombo and M. H. Maathuis · 2014
Cited alongside, same era.
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
T. D. Le, T. Hoang, J. Li, L. Liu, H. Liu, and S. Hu · 2016
Cited alongside, same era.
Elements of Causal Inference - Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
Cited alongside, same era.
Tetrad—a toolbox for causal discovery
J. D. Ramsey, K. Zhang, M. Glymour, R. S. Romero, B. Huang, I. Ebert-Uphoff, S. Samarasinghe, E. A. Barnes, and C. Glymour · 2018
Cited alongside, same era.
DAGs with NO TEARS: Continuous optimization for structure learning
X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing · 2018
Cited alongside, same era.
EconML: A Python package for ml-based heterogeneous treatment effects estimation
K. Battocchi, E. Dillon, M. Hei, G. Lewis, P. Oka, M. Oprescu, and V. Syrgkanis · 2019
Causal discovery toolbox: Uncovering causal relationships in python
D. Kalainathan, O. Goudet, and R. Dutta · 2020
Later among the works it cites.
Gradient-based neural DAG learning
S. Lachapelle, P. Brouillard, T. Deleu, and S. Lacoste-Julien · 2020
Later among the works it cites.
Whynot, 2020
J. Miller, C. Hsu, J. Troutman, J. Perdomo, T. Zrnic, L. Liu, Y. Sun, L. Schmidt, and M. Hardt · 2020
Later among the works it cites.
On the role of sparsity and dag constraints for learning linear dags
I. Ng, A. Ghassami, and K. Zhang · 2020
Later among the works it cites.
DoWhy: An end-to-end library for causal inference
A. Sharma and E. Kiciman · 2020
Later among the works it cites.
An influence-based approach for root cause alarm discovery in telecom networks
K. Zhang, M. Kalander, M. Zhou, X. Zhang, and J. Ye · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Detecting and quantifying causal associations in large nonlinear time series datasets
J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, and D. Sejdinovic · 2019
Cited alongside, same era.
bd2kccd/py-causal, 2019
C. K. Wongchokprasitti, H. Hochheiser, J. Espino, E. Maguire, B. Andrews, M. Davis, and C. Inskip · 2019
Cited alongside, same era.
DAG-GNN: DAG structure learning with graph neural networks
Y. Yu, J. Chen, T. Gao, and M. Yu · 2019
Cited alongside, same era.
Differentiable causal discovery from interventional data
P. Brouillard, S. Lachapelle, A. Lacoste, S. Lacoste-Julien, and A. Drouin · 2020
Cited alongside, same era.
CausalML: Python package for causal machine learning
H. Chen, T. Harinen, J.-Y. Lee, M. Yung, and Z. Zhao · 2020
Cited alongside, same era.
Low rank directed acyclic graphs and causal structure learning
Z. Fang, S. Zhu, J. Zhang, Y. Liu, Z. Chen, and Y. He · 2020
Cited alongside, same era.
Later among the works it cites.
Learning sparse nonparametric DAGs
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. P. Xing · 2020
Later among the works it cites.
Causal discovery with reinforcement learning
S. Zhu, I. Ng, and Z. Chen · 2020
Later among the works it cites.
Differentiable causal discovery under unmeasured confounding
R. Bhattacharya, T. Nagarajan, D. Malinsky, and I. Shpitser · 2021
Closest in time.
THP: Topological Hawkes processes for learning granger causality on event sequences
R. Cai, S. Wu, J. Qiao, Z. Hao, K. Zhang, and X. Zhang · 2021
Closest in time.
Ordering-based causal discovery with reinforcement learning
X. Wang, Y. Du, S. Zhu, L. Ke, Z. Chen, J. Hao, and J. Wang · 2021
Closest in time.
causal-learn: Causal discovery for python
K. Zhang, J. Ramsey, M. Gong, R. Cai, S. Shimizu, P. Spirtes, and C. Glymour · 2021
Closest in time.