Improving Inference from Simple Instruments through Compliance Estimation
通过依从率估计改进简单工具变量的统计推断AI 解读 ↓
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通过依从率估计改进简单工具变量的统计推断
- 研究问题
- 当二元工具变量对二元处理的影响随基线协变量变化时，能否利用这种可预测的第一阶段异质性，提高处理效应估计精度？
- 方法与思路
- 作者提出按估计依从率加权的工具变量估计，允许用机器学习预测依从率，并在潜在结果与局部平均处理效应框架下研究渐近推断。
- 材料陈述的贡献
- 摘要称，该实现允许非参数估计权重后仍进行有效推断；理论与模拟显示，第一阶段异质性存在时，加权可降低估计方差。
- 阅读关注点
- 核对依从率的识别条件、工具变量与协变量独立性的作用、估计权重如何进入标准误，以及加权与未加权目标参数的差异。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出完整正则条件，精度改善也不能消除加权引起的目标参数变化。
原文摘要
Instrumental variables (IV) regression is widely used to estimate causal treatment effects in settings where receipt of treatment is not fully random, but there exists an instrument that generates exogenous variation in treatment exposure. While IV can recover consistent treatment effect estimates, they are often noisy. Building upon earlier work in biostatistics (Joffe and Brensinger, 2003) and relating to an evolving literature in econometrics (including Abadie et al., 2019; Huntington-Klein, 2020; Borusyak and Hull, 2020), we study how to improve the efficiency of IV estimates by exploiting the predictable variation in the strength of the instrument. In the case where both the treatment and instrument are binary and the instrument is independent of baseline covariates, we study weighting each observation according to its estimated compliance (that is, its conditional probability of being affected by the instrument), which we motivate from a (constrained) solution of the first-stage prediction problem implicit to IV. The resulting estimator can leverage machine learning to estimate compliance as a function of baseline covariates. We derive the large-sample properties of a specific implementation of a weighted IV estimator in the potential outcomes and local average treatment effect (LATE) frameworks, and provide tools for inference that remain valid even when the weights are estimated nonparametrically. With both theoretical results and a simulation study, we demonstrate that compliance weighting meaningfully reduces the variance of IV estimates when first-stage heterogeneity is present, and that this improvement often outweighs any difference between the compliance-weighted and unweighted IV estimands. These results suggest that in a variety of applied settings, the precision of IV estimates can be substantially improved by incorporating compliance estimation.