Estimating large causal polytrees from small samples
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Abstract (arXiv)
We consider the problem of estimating a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in gene regulatory networks. We give an algorithm that recovers the tree with high accuracy in such settings. The algorithm works under essentially no distributional or modeling assumptions other than some mild non-degeneracy conditions.
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References · 13
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
- 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
- 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
- 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
- 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
- 2019A simple measure of conditional dependencecited by 38
- 2020A polynomial-time algorithm for learning nonparametric causal graphscited by 29
- 2013The Recovery of Causal Poly-Trees from Statistical Datacited by 19
- 2021Efficient Bayesian network structure learning via local Markov boundary searchcited by 14
- 2021Structure Learning for Directed Treescited by 9
- 2013Score-based Causal Learning in Additive Noise Modelscited by 8
- 2013SparsityBoost: A New Scoring Function for Learning Bayesian Network Structurecited by 7
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- 2021Learning Linear Polytree Structural Equation Modelscited by 1
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