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Causal structure learning has long been the central task of inferring causal insights from data.
Remarks on Some Nonparametric Estimates of a Density Function
Murray Rosenblatt · 1956
Earlier work this paper cites.
Estimating the dimension of a model
Gideon Schwarz · 1978
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Equivalence and synthesis of causal models
Thomas Verma and Judea Pearl · 1990
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An algorithm for fast recovery of sparse causal graphs
Peter Spirtes and Clark Glymour · 1991
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Using galois lattices to represent network data
Linton C. Freeman and Douglas R. White · 1993
Earlier work this paper cites.
Graphical aspects of causal models
T. S. Verma · 1993
Earlier work this paper cites.
A transformational characterization of equivalent bayesian network structures
David Maxwell Chickering · 1995
Earlier work this paper cites.
A characterization of markov equivalence classes for acyclic digraphs
Steen A. Andersson, David Madigan, and Michael D. Perlman · 1997
Earlier work this paper cites.
Causation, Prediction, and Search
P. Spirtes, C. Glymour, and R. Scheines · 2001
Earlier work this paper cites.
Optimal structure identification with greedy search
David M. Chickering · 2002
Earlier work this paper cites.
Synchronous firing and higher-order interactions in neuron pool
Shun-Ichi Amari, Hiroyuki Nakahara, Si Wu, and Yutaka Sakai · 2003
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Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
Earlier work this paper cites.
Finding optimal Bayesian networks by dynamic programming
Ajit P. Singh and Andrew W. Moore · 2005
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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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.
Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman · 2009
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Causality
Judea Pearl · 2009
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On the identifiability of the post-nonlinear causal model
Kun Zhang and Aapo Hyvärinen · 2009
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Learning optimal Bayesian networks using A* search
Changhe Yuan, Brandon Malone, and Xiaojian Wu · 2011
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CAM: Causal additive models, high-dimensional order search and penalized regression
Peter Bühlmann, Jonas Peters, and Jan Ernest · 2014
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High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh and Peter Bühlmann · 2014
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Social contagion models on hypergraphs
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Mohammad Ali Javidian, Zhiyu Wang, Linyuan Lu, and Marco Valtorta · 2020
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Gradient-based neural dag learning
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien · 2020
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How does this interaction affect me? interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, and Yan Liu · 2020
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Learning sparse nonparametric DAGs
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing · 2020
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Sparse interaction additive networks via feature interaction detection and sparse selection
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Causal discovery with continuous additive noise models
Jonas Peters, Joris M. Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
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Homological scaffolds of brain functional networks
G Petri, P Expert, F Turkheimer, R Carhart-Harris, D Nutt, P J Hellyer, and F Vaccarino · 2014
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Higher-order genetic interactions and their contribution to complex traits
Matthew B Taylor and Ian M Ehrenreich · 2015
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Graphs for margins of bayesian networks
Robin J. Evans · 2016
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Integer linear programming for the Bayesian network structure learning problem
Mark Bartlett and James Cussens · 2017
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Higher-order molecular organization as a source of biological function
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Dynamics on higher-order networks: a review
Soumen Majhi, Matjaž Perc, and Dibakar Ghosh · 2022
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Masked gradient-based causal structure learning
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Score matching enables causal discovery of nonlinear additive noise models
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Causal inference with heteroscedastic noise models
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Fast scalable and accurate discovery of DAGs using the best order score search and grow shrink trees
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On the identifiability and estimation of causal location-scale noise models
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Generalized precision matrix for scalable estimation of nonparametric markov networks
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A complete decomposition of kl error using refined information and mode interaction selection, 2024
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