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Causal structure learning (CSL) refers to the task of learning causal relationships from data.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, and Richard Scheines · 2000
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Patrik Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
Earlier work this paper cites.
High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh and Peter Bühlmann · 2014
Earlier work this paper cites.
Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens
Atray Dixit, Oren Parnas, Biyu Li, Jenny Chen, Charles P Fulco, Livnat Jerby-Arnon, Nemanja D Marjanovic, Danielle Dionne, Tyler Burks, Raktima Raychowdhury, et al · 2016
Earlier work this paper cites.
Inferring causal molecular networks: empirical assessment through a community-based effort
Steven M Hill, Laura M Heiser, Thomas Cokelaer, Michael Unger, Nicole K Nesser, Daniel E Carlin, Yang Zhang, Artem Sokolov, Evan O Paull, Chris K Wong, et al · 2016
Earlier work this paper cites.
Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Earlier work this paper cites.
On wasserstein two-sample testing and related families of nonparametric tests
Aaditya Ramdas, Nicolás García Trillos, and Marco Cuturi · 2017
Earlier work this paper cites.
Permutation-based causal inference algorithms with interventions
Yuhao Wang, Liam Solus, Karren Yang, and Caroline Uhler · 2017
Earlier work this paper cites.
Causal structure learning
Christina Heinze-Deml, Marloes H Maathuis, and Nicolai Meinshausen · 2018
Earlier work this paper cites.
Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
Earlier work this paper cites.
A general and flexible method for signal extraction from single-cell rna-seq data
Davide Risso, Fanny Perraudeau, Svetlana Gribkova, Sandrine Dudoit, and Jean-Philippe Vert · 2018
Cited alongside, same era.
Evaluation of causal structure learning algorithms via risk estimation
Marco Eigenmann, Sach Mukherjee, and Marloes Maathuis · 2020
Cited alongside, same era.
Sergio: a single-cell expression simulator guided by gene regulatory networks
Payam Dibaeinia and Saurabh Sinha · 2020
Cited alongside, same era.
Pangaea: A modular and extensible collection of tools for mining context dependent gene relationships from the biomedical literature
Liviu Pirvan and Shamith A Samarajiwa · 2020
Cited alongside, same era.
Efficient neural causal discovery without acyclicity constraints
Phillip Lippe, Taco Cohen, and Efstratios Gavves · 2021
Cited alongside, same era.
Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq
Joseph M Replogle, Reuben A Saunders, Angela N Pogson, Jeffrey A Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J Wagner, Karen Adelman, Gila Lithwick-Yanai, et al · 2022
Later among the works it cites.
Causalbench: A large-scale benchmark for network inference from single-cell perturbation data
Mathieu Chevalley, Yusuf Roohani, Arash Mehrjou, Jure Leskovec, and Patrick Schwab · 2022
Later among the works it cites.
Interventions, where and how? experimental design for causal models at scale
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, and Stefan Bauer · 2022
Later among the works it cites.
Deep learning of causal structures in high dimensions under data limitations
Kai Lagemann, Christian Lagemann, Bernd Taschler, and Sach Mukherjee · 2023
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Differentiable multi-target causal bayesian experimental design
Panagiotis Tigas, Yashas Annadani, Desi R Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, and Stefan Bauer · 2023
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Beware of the simulated dag! causal discovery benchmarks may be easy to game
Alexander Reisach, Christof Seiler, and Sebastian Weichwald · 2021
Cited alongside, same era.
Near-optimal multi-perturbation experimental design for causal structure learning
Scott Sussex, Caroline Uhler, and Andreas Krause · 2021
Cited alongside, same era.
Consistency guarantees for greedy permutation-based causal inference algorithms
Liam Solus, Yuhao Wang, and Caroline Uhler · 2021
Cited alongside, same era.
Large-scale differentiable causal discovery of factor graphs
Romain Lopez, Jan-Christian Hütter, Jonathan Pritchard, and Aviv Regev · 2022
Cited alongside, same era.
Learning to induce causal structure
Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Anirudh Goyal, Jorg Bornschein, Melanie Rey, Theophane Weber, Matthew Botvinic, Michael Mozer, and Danilo Jimenez Rezende · 2022
Cited alongside, same era.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors
Cited in the paper.
Later among the works it cites.
Bacadi: Bayesian causal discovery with unknown interventions
Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, and Andreas Krause · 2023
Later among the works it cites.
Groundgan: Grn-guided simulation of single-cell rna-seq data using causal generative adversarial networks
Yazdan Zinati, Abdulrahman Takiddeen, and Amin Emad · 2024
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Structure learning with continuous optimization: A sober look and beyond
Ignavier Ng, Biwei Huang, and Kun Zhang · 2024
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Entrez direct: E-utilities on the unix command line
Jonathan Kans · 2024
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