Proxy-Adjusted Causal Discovery from Targeted Interventions

2026-09-20

AI Summary

本文提出了一种非参数框架PABM，通过目标干预、响应数据和代理变量来恢复有向无环图，解决了随机扰动无法识别直接因果关系的问题。

Abstract

Randomized perturbations can reveal downstream responses without identifying which causal relationships are direct. Conditioning on intermediate responses may induce associations through unmeasured common causes, even when the source is randomized. We introduce Proxy-Adjusted Balanced Masking (PABM), a nonparametric framework for recovering directed acyclic graphs from targeted interventions, response data, and recorded proxies. PABM uses valid target-specific interventions to identify candidate ancestors. It then compares conditional target distributions with and without a candidate response and, when supported by the design, its intervention variable, adjusting for other ancestors and proxies. Each comparison uses matched observations, adjustment variables, and fitting procedures. Identification requires every included nonparent comparison to have zero conditional gain and each parent to have positive gain in at least one comparison. We establish population identification, finite-sample recovery conditions that accommodate incomplete adjustment and estimation error, and familywise error control under valid held-out p-values. Continuous-response simulations show favorable graph recovery relative to specified comparator pipelines; K562-calibrated count simulations reveal a selection--ranking tradeoff. Proxy ablations assess sensitivity to recorded information. An analysis of K562 Perturb-seq data illustrates descriptive candidate-network construction; proxy adequacy for unmeasured biological variation remains unresolved.

Problem

• Causal Discovery
• Targeted Interventions
• Proxy-Adjusted
• Directed Acyclic Graphs
• Unmeasured Common Causes

Innovation

• Proxy-Adjusted Balanced Masking
• Directed Acyclic Graphs
• Targeted Interventions
• Conditional Gain
• Nonparametric Framework