用生成流网络学习因果图结构，保留不确定性避免错误决策
Generative Flow Networks: Theory and Applications to Structure Learning
- 将图生成视为逐步决策过程，建模因果图分布
- 可从观测与实验数据中近似因果模型后验分布
- 适合需要可信因果推断的研究者，如医疗或金融领域
在不假设数据生成机制的前提下，多个因果模型可能同样解释观测数据。为避免因选择单一任意模型而导致危险决策，必须保持对潜在模型的认知不确定性。本论文从贝叶斯视角研究结构学习问题，旨在基于数据近似因果模型结构（以有向无环图DAG表示）的后验分布。提出生成流网络（GFlowNets），一种新型概率模型，用于建模离散且组合对象（如图）的分布。该方法将生成过程视为序列决策问题，分步构建目标分布（归一化常数未知）的样本。论文第一部分建立GFlowNets的数学基础，揭示其与变分推断、强化学习等领域的联系，并拓展至非离散问题。第二部分展示如何利用GFlowNets，结合观测与实验数据，近似因果贝叶斯网络的DAG结构及其因果机制参数的后验分布。
原文摘要 · Abstract (English)
Without any assumptions about data generation, multiple causal models may explain our observations equally well. To avoid selecting a single arbitrary model that could result in unsafe decisions if it does not match reality, it is therefore essential to maintain a notion of epistemic uncertainty about our possible candidates. This thesis studies the problem of structure learning from a Bayesian perspective, approximating the posterior distribution over the structure of a causal model, represented as a directed acyclic graph (DAG), given data. It introduces Generative Flow Networks (GFlowNets), a novel class of probabilistic models designed for modeling distributions over discrete and compositional objects such as graphs. They treat generation as a sequential decision making problem, constructing samples of a target distribution defined up to a normalization constant piece by piece. In the first part of this thesis, we present the mathematical foundations of GFlowNets, their connections to existing domains of machine learning and statistics such as variational inference and reinforcement learning, and their extensions beyond discrete problems. In the second part of this thesis, we show how GFlowNets can approximate the posterior distribution over DAG structures of causal Bayesian Networks, along with the parameters of its causal mechanisms, given observational and experimental data.
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