提出共享状态干扰下的因果推断新方法，提升市场与推荐系统中的效果评估精度。
Double Machine Learning for Causal Inference under Shared-State Interference
- 基于共享状态条件独立假设，扩展双重机器学习框架
- 在多种模型下实现平均直接效应与全局处理效应的高效估计
- 适用于有市场交互或算法推荐的场景，适合政策评估与产品优化
研究人员和从业者常需在单位通过市场和推荐系统相互影响的场景中测量处理效应。在此类场景中，单位受某些共享状态（如价格、算法推荐或社交信号）影响。本文形式化这一结构，称为共享状态干扰，并认为该设定可涵盖众多实际应用场景。核心建模假设是：个体的潜在结果在给定共享状态条件下相互独立。随后，我们证明了双重机器学习（DML）定理的一个扩展，给出了在共享状态干扰下实现高效推断的条件。同时，在若干感兴趣模型中实例化该通用定理，证明了平均直接效应（ADE）或全局平均处理效应（GATE）可被高效估计。
原文摘要 · Abstract (English)
Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected by certain shared states, like prices, algorithmic recommendations or social signals. We formalize this structure, calling it shared-state interference, and argue that our formulation captures many relevant applied settings. Our key modeling assumption is that individuals' potential outcomes are independent conditional on the shared state. We then prove an extension of a double machine learning (DML) theorem providing conditions for achieving efficient inference under shared-state interference. We also instantiate our general theorem in several models of interest where it is possible to efficiently estimate the average direct effect (ADE) or global average treatment effect (GATE).
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