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高斯变换建模与分布回归函数估计AI 解读

We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative…

研究者与社会偏好不一致时的最优估计AI 解读

Econometric analysis typically focuses on the statistical properties of fixed estimators and ignores researcher choices. In this article, I instead approach the analysis of experimental data as a mechanism‐design problem that acknowledges that researchers choose between estimators, sometimes based on the data and often according to their own preferences. Specifically, I focus on covariate adjustments, which can increase the precision of a treatment‐effect estimate, but open…

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异质性处理效应机器学习推断评论：序贯验证方法AI 解读

We use the martingale construction of Luedtke and van der Laan (2016) to develop tests for the presence of treatment heterogeneity. The resulting sequential validation approach can be instantiated using various validation metrics, such as BLPs, GATES, QINI curves, etc., and provides an alternative to cross‐validation‐like cross‐fold application of these metrics. This note was prepared as a comment on the Fisher–Schultz paper by Chernozhukov, Demirer, Duflo, and…

异质性处理效应机器学习推断评论：随机化推断方法AI 解读

We examine the split‐sample robust inference (SSRI) methodology introduced by Chernozhukov, Demirer, Duflo, and Fernandez‐Val for quantifying uncertainty in heterogeneous treatment effect estimates produced by machine learning (ML) models. Although SSRI properly accounts for the additional variability due to sample splitting, its computational cost becomes prohibitive with complex ML models. We propose an alternative approach based on randomization inference (RI) that…

随机实验中异质性处理效应的通用机器学习推断及印度免疫接种应用AI 解读

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups , and average characteristics of most and least impacted units . The approach is valid in high‐dimensional settings, where the effects are proxied (but not necessarily consistently estimated) by predictive and causal…

让补贴发挥作用：规则与自由裁量AI 解读

We estimate the employment effects of a large program of public investment subsidies to private firms that ranked applicants on a score reflecting both objective rules and local politicians' discretion. Leveraging the rationing of funds as an ideal Regression Discontinuity Design, we characterize the heterogeneity of treatment effects and cost‐per‐new‐job across inframarginal firms and estimate the cost‐effectiveness of subsidies under factual and counterfactual allocations.…

平均处理效应的双重稳健贝叶斯推断AI 解读

We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness. For our new Bayesian approach, we first adjust the prior distributions of the conditional mean functions, and then correct the posterior distribution of the resulting ATE. Both adjustments make use of pilot estimators motivated by the semiparametric influence function for ATE estimation. We prove asymptotic equivalence of our Bayesian procedure and efficient…

候选人综合素质的估计AI 解读

We estimate valence measures of candidates running in U.S. House elections from data on vote shares. Our identification and estimation strategy builds on ideas developed for estimating production functions, allowing us to control for possible endogeneity of campaign spending and sample selection of candidates due to endogenous entry. We find that incumbents have substantially higher valence measures than challengers running against them, resulting in about 3.5…

包含新处理方案的政策学习AI 解读

I study the problem of a decision maker choosing a policy that allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear‐ and…

交互固定效应高维面板模型的估计与推断AI 解读

We develop new econometric methods for estimation and inference in high‐dimensional panel data models with interactive fixed effects. Our approach can be regarded as a nontrivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross‐sectionally averaged…

有限样本下动态离散博弈的同质性检验AI 解读

The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time. We refer to this as the “homogeneity assumption” in dynamic discrete games. This assumption enables empirical studies to estimate the game's structural parameters by pooling data from multiple markets and from many time periods. In this paper, we propose a hypothesis test to evaluate whether the…

随机系数潜在效用模型的识别AI 解读

This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete choice and models of multiple purchases. We establish identification using variation in mean quantities. The results apply even when an analyst observes only aggregate demands but not whether goods are chosen together. The identification results use exclusion restrictions on covariates and independence between random slope coefficients…

政策约束下最优动态处理分配规则的估计AI 解读

Many policies involve dynamics in their treatment assignments where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or…

动态有序面板 Logit 模型AI 解读

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper presents sufficient conditions for the moment conditions to identify the common parameters of the model, namely the regression coefficients, the autoregressive parameters, and the…

商品市场中的储存作用：基于粮食数据的间接推断AI 解读

We develop an indirect inference approach relying on a linear supply and demand model serving as an auxiliary model to provide the first full empirical test of the rational expectations commodity storage model. We build a rich storage model that incorporates a supply response and four structural shocks and show that exploiting information on both prices and quantities is critical for relaxing previous restrictive identifying assumptions and assessing the empirical…

教育与岗位纵向错配的经济后果AI 解读

We study two first‐order economic consequences of vertical mismatch, using a simple (neoclassical) model of under and overemployment. Individuals of high type can perform both skilled and unskilled jobs, but only a fraction of low‐type workers can perform skilled jobs. People have different costs over these jobs. First, we calibrate the model to match U.S. CPS time series since the 1980s. To control for unobserved heterogeneity, we compute wages based on workers who have…

单调最佳反应博弈经验分析的序数方法AI 解读

We develop a nonparametric and ordinal approach for testing pure strategy Nash equilibrium play in games with monotone best responses, such as those with strategic complements/substitutes. The approach makes minimal assumptions on unobserved heterogeneity, requires no parametric assumptions on payoff functions, and no restriction on equilibrium selection from multiple equilibria. The approach can also be extended in order to make inferences and predictions. Both…

教学能否被教会？秘鲁农村教师教学技能的大规模提升AI 解读

We evaluate the impact of a large‐scale teacher coaching program in Peru, a context with high teacher turnover, on teachers' pedagogical skills and student learning. Previous studies find that small‐scale coaching programs can improve teaching of reading and science in developing countries. However, scaling up can reduce programs' effectiveness, and teacher turnover can erode compliance and cause spillovers onto non‐program schools. We develop a framework that defines…

多维类型保险模型的计量分析AI 解读

In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurees choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification…

匹配数量随样本量增长的倾向得分匹配AI 解读

Abstract This paper re-examines the work of Abadie & Imbens (2016) on propensity score matching for average treatment effect estimation. We explore the asymptotic behaviour of these estimators when the number of nearest neighbours, M, grows with the sample size. It is shown, while not surprising, but technically nontrivial, that the modified estimators can improve upon the original fixed M-estimators in terms of efficiency. Additionally, we demonstrate the potential to…

高维生存数据最优治疗策略的无模型变量筛选AI 解读

Summary We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with…

连续暴露与二元结局匹配观察研究的敏感性分析AI 解读

Summary Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion’s sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies.…

序数结局下观察研究与不完全随机试验中获益指标的尖锐符号非参数界AI 解读

Abstract The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and…