Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
条件矩限制下的机器学习去偏：局部平均处理效应应用AI 解读 ↓
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条件矩限制下的机器学习去偏：局部平均处理效应应用
- 研究问题
- 当条件矩模型含有机器学习估计的无限维成分时，如何构造局部去偏且有效的矩条件，并获得具有因果含义的处理效应估计？
- 方法与思路
- 作者刻画一般半参数条件矩模型中局部去偏矩的存在性与相关性，并在内生处理情境下提出基于正交矩的依从性机器学习估计量。
- 材料陈述的贡献
- 摘要称，CML 的目标量可表示为条件 LATE 的凸组合，具有第一阶段局部稳健性；俄勒冈医保应用提示医疗利用的正向效应更大。
- 阅读关注点
- 应核对去偏矩存在的充要条件、工具变量相关性与因果识别条件，以及 CML 的权重构造、推断方法和实证比较口径。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收均未核实；摘要未列出完整识别条件、收敛要求或医保应用的效应数值。
原文摘要
Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined.