A polynomial-time algorithm for learning nonparametric causal graphs
- URL: http://arxiv.org/abs/2006.11970v2
- Date: Tue, 10 Nov 2020 21:35:55 GMT
- Title: A polynomial-time algorithm for learning nonparametric causal graphs
- Authors: Ming Gao, Yi Ding, Bryon Aragam
- Abstract summary: The analysis is model-free and does not assume linearity, additivity, independent noise, or faithfulness.
We impose a condition on the residual variances that is closely related to previous work on linear models with equal variances.
- Score: 18.739085486953698
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We establish finite-sample guarantees for a polynomial-time algorithm for
learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from
data. The analysis is model-free and does not assume linearity, additivity,
independent noise, or faithfulness. Instead, we impose a condition on the
residual variances that is closely related to previous work on linear models
with equal variances. Compared to an optimal algorithm with oracle knowledge of
the variable ordering, the additional cost of the algorithm is linear in the
dimension $d$ and the number of samples $n$. Finally, we compare the proposed
algorithm to existing approaches in a simulation study.
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