每日简报

连接分层与回归调整：随机实验中的批次自适应分层与设计后调整AI 解读

To increase statistical efficiency in a randomized experiment, researchers often use stratification (i.e., blocking) in the design stage. However, conventional practices of stratification fail to exploit valuable information about the predictive relationship between covariates and potential outcomes. In this paper, I introduce an adaptive stratification procedure for increasing statistical efficiency when some information is available about the relationship between…

面板向量自回归的因果解释AI 解读

This paper discusses the different contemporaneous causal interpretations of Panel Vector Autoregressions (PVAR). I show that the interpretation of PVARs depends on the distribution of the causing variable, and can range from average treatment effects, to average causal responses, to a combination of the two. If the researcher is willing to postulate a no residual autocorrelation assumption, and some units can be thought of as controls, PVAR can identify average treatment…

Control VAR：基于反事实的宏观经济推断方法AI 解读

This paper addresses the challenges of giving a causal interpretation to vector autoregressions (VARs). I show that under independence assumptions VARs can identify average treatment effects, average causal responses, or a mix of the two, depending on the distribution of the policy. But what about situations in which the economist cannot rely on independence assumptions? I propose an alternative method, defined as control-VAR, which uses control variables to estimate causal…

通过最小化 Bregman 散度实现直接去偏机器学习AI 解读

We develop a direct debiased machine learning framework comprising Neyman targeted estimation and generalized Riesz regression. Our framework unifies Riesz regression for automatic debiased machine learning, covariate balancing, targeted maximum likelihood estimation (TMLE), and density-ratio estimation. In many problems involving causal effects or structural models, the parameters of interest depend on regression functions. Plugging regression functions estimated by machine…

Large language models are increasingly used to classify text at scale. Because their outputs are stochastic, it is common to query a model several times per item and aggregate the results, and latent class models offer a principled way to convert such repeated ratings into estimates of outcome prevalence and of the model's error rates. These models assume that repeated ratings of the same item are conditionally independent given the true label. We examine this assumption…

稀疏双边互动模型中的互补性AI 解读

This paper studies complementarities in two-sided interaction models when only a sparse subset of potential matches is realized. I introduce the Tukey model, adding one complementarity parameter to two-way fixed effects. Variation within a four-cycle in the observed matching network identifies this parameter, while connectedness permits identification of agent productivities. I propose a cycle-based estimator that avoids estimating latent productivities. The estimator is…

学习该学什么：结合实验与观察证据的实验设计AI 解读

Experiments deliver credible treatment-effect estimates but, because they are costly, are often restricted to specific sites, small populations, or particular mechanisms. A common practice across several fields is therefore to combine experimental estimates with reduced-form or structural external (observational) evidence to answer broader policy questions, such as those involving general equilibrium effects or external validity. We develop a unified framework for the design…

存在跨簇干扰的整群随机试验AI 解读

The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This paper develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded…

基于分层的非线性效应工具变量分析框架AI 解读

Nonlinear causal effects are prevalent in many research scenarios involving continuous exposures, and instrumental variables (IVs) can be employed to investigate such effects, particularly in the presence of unmeasured confounders. However, common IV methods for nonlinear effect analysis, such as IV regression or the control-function method, have inherent limitations, leading to either low statistical power or potentially misleading conclusions. In this work, we propose an…

平均斯勒茨基对称性的不可检验性AI 解读

Slutsky symmetry and negative semidefiniteness are necessary and sufficient conditions for the rationality of demand functions. While the empirical implications of Slutsky negative semidefiniteness in repeated cross-sectional demand data are well understood, the empirical content of Slutsky symmetry remains largely unexplored. This paper takes an important first step toward addressing this gap. We show that the average Slutsky matrix is not identified and that its identified…

存在潜变量时非参数因果效应估计的局部协变量选择AI 解读

Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our…