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何时能够进行奈曼正交推断？存在性与信息相关性AI 解读

Neyman-orthogonal moments underpin debiased and double machine learning, yet their existence and informativeness are typically presumed. We ask three questions: when do nontrivial orthogonal moments exist, which target directions contain first-order information, and does a proposed moment detect all such directions? Restricted local overidentification characterizes existence under attainability. Effective Fisher information determines relevance; its rank counts informative…

不可观测异质性的去偏机器学习：高维面板与测量误差模型AI 解读

Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedures for valid inference on functionals of UH, allowing for partial identification of multivariate target and high-dimensional nuisance parameters. Our main contribution is a full characterization of all relevant Neyman-orthogonal moments in models with nonparametric UH, where relevance means…

一阶差分真的是随机分配的吗？AI 解读

We consider a first-difference regression of an outcome evolution $ΔY$ on a treatment evolution $ΔD$. If the treatment effect changes over time, the regression residual is a function of the period-one treatment $D_{1}$. Then, researchers should test if $ΔD$ and $D_{1}$ are correlated: if they are, the regression may suffer from an omitted variable bias. To solve it, researchers may control for $E(ΔD|D_{1})$. We revisit Acemoglu et al (2016), who study the effect of imports…

一般条件矩下结构参数的自动去偏机器学习AI 解读

This paper proposes a method to automatically construct or estimate Neyman-orthogonal moments in general models defined by a finite number of conditional moment restrictions (CMRs), with possibly different conditioning variables and endogenous regressors. CMRs are allowed to depend on non-parametric components, which might be flexibly modeled using Machine Learning tools, and non-linearly on finite-dimensional parameters. The key step in this construction is the estimation…

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

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…