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Sep, 2020
面向可扩展的因果DAGs贝叶斯学习
Towards Scalable Bayesian Learning of Causal DAGs
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Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
TL;DR
本文提出了贝叶斯因果推断的方法，其中用到了MCMC方法来进行图结构的采样，并得到了线性高斯DAG模型的因果效应估计。
Abstract
We give methods for
bayesian inference
of
directed acyclic graphs
, DAGs, and the induced
causal effects
from passively observed complete d
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