用贝叶斯优化加速因果图发现，更准更快。
Causal Discovery via Bayesian Optimization
- 用贝叶斯优化搜索高得分因果图，智能选方向
- 比现有方法少试几次就找到准确因果结构
- 适合需要高效因果推断的研究者
现有基于评分的有向无环图（DAG）学习方法在观测数据上恢复因果图时，准确性和采样效率均不足。本文提出DrBO（DAG恢复通过贝叶斯优化）——一种新型DAG学习框架，利用贝叶斯优化（BO）寻找高分DAG。通过精心选择待探索的候选图，能更高效地发现更高得分的图。为解决传统贝叶斯优化在DAG学习中的可扩展性问题，我们用持续训练的丢弃神经网络替代常用的高斯过程，实现：(i)灵活建模DAG评分且避免过拟合，(ii)将不确定性融入评分估计，(iii)随评估次数增长而可扩展。结果表明，DrBO计算高效，在更少试验和更短时间内即能准确恢复因果图。在多种具有挑战性的合成与真实数据集上进行了广泛实验验证。代码已开源：https://github.com/baosws/DrBO。
原文摘要 · Abstract (English)
Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayesian Optimization)-a novel DAG learning framework leveraging Bayesian optimization (BO) to find high-scoring DAGs. We show that, by sophisticatedly choosing the promising DAGs to explore, we can find higher-scoring ones much more efficiently. To address the scalability issues of conventional BO in DAG learning, we replace Gaussian Processes commonly employed in BO with dropout neural networks, trained in a continual manner, which allows for (i) flexibly modeling the DAG scores without overfitting, (ii) incorporation of uncertainty into the estimated scores, and (iii) scaling with the number of evaluations. As a result, DrBO is computationally efficient and can find the accurate DAG in fewer trials and less time than existing state-of-the-art methods. This is demonstrated through an extensive set of empirical evaluations on many challenging settings with both synthetic and real data. Our implementation is available at https://github.com/baosws/DrBO.
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