Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.

翻译：结构学习是因果推断的核心。值得注意的是，当数据稀缺时，因果发现算法往往表现出脆弱性，可能推断出与专家知识相矛盾的因果关系——尤其是在考虑潜变量混淆因素的情况下。更棘手的是，大多数因果发现方法不提供不确定性估计，导致用户难以解释结果并改进推断过程。令人惊讶的是，尽管因果发现本质上是面向人类的活动，但目前尚无研究专注于构建既能1）输出可被专家验证的不确定性估计，又能2）与专家交互以迭代优化因果发现的方方法。为解决这些问题，我们首先提出基于评分函数（如贝叶斯信息准则）构建信念分布，并利用生成流网络对（因果）祖先图进行比例采样。接着，我们利用候选图的多样性，引入最优实验设计，通过迭代询问专家关于变量间关系的问题，有效降低我们对祖先图信念的不确定性。最后，我们通过重要性采样更新样本以融入人类反馈。重要的是，本方法无需因果充分性假设（即允许存在未观测的混淆变量）。使用合成观测数据的实验表明，我们的方法能够准确对祖先图分布进行采样，并且通过人类辅助可显著提升推断质量。