论文标题
合成干预措施
Synthetic Interventions
论文作者
论文摘要
合成控制（SC）方法论是面板数据应用中政策评估的重要工具。研究人员通常使用低级别矩阵因子模型证明SC框架是合理的，该模型假设潜在的结果由低维单元和特定时间的潜在因素描述。在[Abadie '20]的最新工作中，SC方法的开创性作者之一提出了一个问题，即如何将SC框架扩展到多种治疗方法。本文为我们称为合成干预措施（SI）的开放问题提供了一个决议。 SI框架的基础是一个低级张量因子模型，该模型通过在治疗上包括潜在分解来扩展矩阵因子模型。在此模型下，我们提出了基于标准SC的估计器的概括。我们证明了一种实例化方法的一致性，并提供了渐近正常的条件。此外，我们进行了代表性的模拟，以研究其预测性能，并重新审视[Abadie-Diamond-Hainmueller '10]对反烟科立法的影响，通过探索先前未调查的相关问题。
The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications. Researchers commonly justify the SC framework with a low-rank matrix factor model that assumes the potential outcomes are described by low-dimensional unit and time specific latent factors. In the recent work of [Abadie '20], one of the pioneering authors of the SC method posed the question of how the SC framework can be extended to multiple treatments. This article offers one resolution to this open question that we call synthetic interventions (SI). Fundamental to the SI framework is a low-rank tensor factor model, which extends the matrix factor model by including a latent factorization over treatments. Under this model, we propose a generalization of the standard SC-based estimators. We prove the consistency for one instantiation of our approach and provide conditions under which it is asymptotically normal. Moreover, we conduct a representative simulation to study its prediction performance and revisit the canonical SC case study of [Abadie-Diamond-Hainmueller '10] on the impact of anti-tobacco legislations by exploring related questions not previously investigated.
