Proximal Causal Inference for Synthetic Control with Surrogates
利用替代指标的合成控制近端因果推断AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究政策评估中的合成控制、反事实结果与处理组平均处理效应，并以近端推断建立识别，直接对应因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
利用替代指标的合成控制近端因果推断
- 研究问题
- 当干预前时期较短或干预后时期较长时，如何利用干预后与因果效应相关的时变替代指标，改善合成控制的反事实预测和效应估计？
- 方法与思路
- 作者将替代指标纳入合成控制，通过近端推断框架建立参数识别条件，并用广义矩估计开展处理组平均处理效应的估计与推断。
- 材料陈述的贡献
- 摘要称特定条件下仅用干预后数据即可估计，并扩展到协变量调整、非参数识别和受污染替代指标；模拟报告了效应估计与推断的改善。
- 阅读关注点
- 应核对替代指标的有效性条件、仅用干预后数据的识别依据、受污染指标的修正方式，以及模拟比较和1907年金融恐慌应用的设计。
- 阅读边界
- 这是预印本，不能据此认定已同行评审或期刊接收。摘要未说明具体识别条件、误差界和比较数值，不能判断优势适用的范围。
原文摘要
The synthetic control method (SCM) has become a popular tool for estimating causal effects in policy evaluation, where a single treated unit is observed, and a heterogeneous set of untreated units with pre- and post-policy change data are also observed. However, the synthetic control method faces challenges in accurately predicting post-intervention potential outcome had, contrary to fact, the treatment been withheld, when the pre-intervention period is short or the post-intervention period is long. To address these issues, we propose a novel method that leverages post-intervention information, specifically time-varying correlates of the causal effect called "surrogates", within the synthetic control framework. We establish conditions for identifying model parameters using the proximal inference framework and apply the generalized method of moments (GMM) approach for estimation and inference about the average treatment effect on the treated (ATT). Interestingly, we uncover specific conditions under which exclusively using post-intervention data suffices for estimation within our framework. Moreover, we explore several extensions, including covariates adjustment, relaxing linearity assumptions through non-parametric identification, and incorporating so-called "contaminated" surrogates, which do not exactly satisfy conditions to be valid surrogates but nevertheless can be incorporated via a simple modification of the proposed approach. Through a simulation study, we demonstrate that our method can outperform other synthetic control methods in estimating both short-term and long-term effects, yielding more accurate inferences. In an empirical application examining the Panic of 1907, one of the worst financial crises in U.S. history, we confirm the practical relevance of our theoretical results.