发表机构
Department of Biostatistics, Epidemiology and Informatics; University of Pennsylvania（生物统计、流行病学与信息学系; 宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个无模型且约束查询最优的统计推断框架，利用单目标干预在存在潜在变量和选择偏差时进行因果发现，并通过极大祖先图建立可识别性，开发两阶段推断程序控制族错误率，在A549细胞数据上验证。
AI 中文摘要
在存在潜在混杂和选择偏差的情况下，从观测数据和干预数据中进行因果发现变得具有挑战性，因为此时因果结构不再能由观测变量上的有向无环图充分表示。现有的无模型方法通常依赖于指数数量的条件独立性检验，并在高维设置中提供有限的不确定性量化。我们开发了一个无模型且约束查询最优的统计推断框架，用于在存在潜在变量和选择的情况下，通过单目标干预进行因果发现。我们引入了系统诱导子图（SIS）来捕捉系统变量之间的因果关系，同时考虑上下文变量。我们通过极大祖先图（MAGs）建立了其可识别性，并表明对每个观测系统变量的干预对于唯一识别是充分的，并且在最坏情况下是必要的。基于这些结果，我们开发了一个两阶段图推断程序，在第一阶段功效足够的情况下，具有渐近的族错误率控制。对于$d_X$个观测系统变量，该程序最多需要$\frac{5}{2}d_X^2$次统计检验，可在每个阶段内并行化，并实现了最优的约束查询复杂度（常数因子内）。该框架支持软干预，并避免了参数结构方程假设。我们通过分析干扰素-$\beta$刺激的A549肺癌细胞系的Perturb-seq数据来展示这些方法。
英文摘要
Causal discovery from observational and interventional data becomes challenging in the presence of latent confounding and selection bias, where causal structure is no longer adequately represented by directed acyclic graphs over observed variables. Existing model-free methods often rely on an exponential number of conditional independence tests and provide limited uncertainty quantification in high-dimensional settings. We develop a model-free and constraint-query optimal statistical inference framework for causal discovery under latent variables and selection using single-target interventions. We introduce the system-induced subgraph (SIS) to capture the causal relations among system variables while accounting for context variables. We establish its identifiability through maximal ancestral graphs (MAGs), and show that interventions on each observed system variable are sufficient for unique identification and necessary in the worst case. Building on these results, we develop a two-stage graph inference procedure with asymptotic family-wise error control under sufficient first-stage power. For $d_X$ observed system variables, the procedure requires at most $\frac{5}{2}d_X^2$ statistical tests, parallelizable within each stage, and achieves optimal constraint-query complexity up to a constant factor. The framework accommodates soft interventions and avoids parametric structural equation assumptions. We illustrate the methods through analysis of Perturb-seq data from interferon-$β$-stimulated A549 lung cancer cell lines.
