每日简报

教育回报从何而来？教育阶段转换与劳动力市场收益AI 解读

Conventional research on educational effects typically either employs a "years of schooling" measure of education, or dichotomizes attainment as a point-in-time treatment. Yet, such a conceptualization of education is misaligned with the sequential process by which individuals make educational transitions. In this paper, I propose a causal mediation framework for the study of educational effects on outcomes such as earnings. The framework considers the effect of a given…

网络干扰下的因果推断AI 解读

We review recent advances in causal inference under interference, drawing on a complex and diverse body of work ranging from causal inference, network science, the health sciences, economics, and the social sciences. Interference in connected populations implies that the treatment assignments of units can affect the outcomes of other units directly (via spillover) and indirectly (via contagion). Examples include public health interventions, economic and financial…

聚类线性模型中的估计与排除限制AI 解读

We study linear regression models with clustered data, high-dimensional controls, and intricate exclusion restrictions. We propose a correctly centered internal instrument IV estimator that accommodates a broad class of exclusion restrictions and allows within-cluster dependence. The estimator admits a simple leave-out interpretation and is computationally tractable. We derive a central limit theorem for the associated quadratic form and propose a robust variance estimator.…

因果测地学：沿相关与因果之间的路径估计反事实AI 解读

We introduce causal geodesy, a framework for studying the landscape of stochastic interventions that lie between the two extremes of performing no intervention and performing a sharp intervention that sets an exposure equal to a specific value. We define this framework by constructing paths of distributions that smoothly interpolate between the treatment density and a point mass at the target intervention. Thus, each path starts at a purely observational (or correlational)…

基于协变量平衡倾向得分的双重差分估计量AI 解读

This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel data are available. We show that the proposed covariate balancing propensity score (CBPS) DID estimator possesses several desirable properties: (i) local efficiency, (ii) double robustness in terms of consistency, (iii) double robustness in terms of inference, and (iv) faster convergence to the ATT…

平行趋势违背下双重差分政策评估的贝叶斯敏感性分析AI 解读

Violations of the parallel trends assumption pose significant challenges for causal inference in difference-in-differences (DiD) studies, especially in policy evaluations where pre-treatment dynamics and external shocks may bias estimates. In this work, we propose a Bayesian DiD framework to allow us to estimate the effect of policies when parallel trends is violated. To address potential deviations from the parallel trends assumption, we introduce a formal sensitivity…

二元结果分层实验中平均处理效应的精确与保守推断AI 解读

We extend methods for finite-sample inference about the average treatment effect (ATE) in randomized experiments with binary outcomes to accommodate stratification (blocking). We present three valid methods that differ in their computational and statistical efficiency. The first method constructs conservative, Bonferroni-adjusted confidence intervals separately for the mean response in the treatment and control groups in each stratum, then takes appropriate weighted…

剂量—暴露—响应建模对剂量响应估计与优化的收益AI 解读

In randomized dose-finding trials, although drug exposure data form a part of key information for dose selection, the evaluation of the dose-response (DR) relationship often mainly uses DR data. We examine the benefit of dose-exposure-response (DER) modeling by sequentially modeling the dose-exposure (DE) and exposure-response (ER) relationships in parameter estimation and prediction, compared with direct DR modeling without PK data. We consider ER modeling approaches with…

以剂量为工具变量、利用暴露数据稳健估计因果剂量响应关系AI 解读

An accurate estimation of the dose-response relationship is important to determine the optimal dose. For this purpose, a dose finding trial in which subjects are randomized to a few fixed dose levels is the most commonly used design. Often, the estimation uses response data only, although drug exposure data are often obtained during the trial. The use of exposure data to improve this estimation is difficult, as exposure-response relationships are typically subject to…

同伴效应估计中的弱识别AI 解读

It is commonly accepted that some phenomena are social: for example, individuals' smoking habits often correlate with those of their peers. Such correlations can have a variety of explanations, such as direct contagion or shared socioeconomic circumstances. The network linear-in-means model is a workhorse statistical model which incorporates these peer effects by including average neighborhood characteristics as regressors. Although the model's parameters are identifiable…

DFW：用于协变量平衡与处理效应估计的新加权方案AI 解读

Estimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups. Propensity score-based weighting methods are widely used to address this issue by reweighting samples to simulate a randomized controlled trial (RCT). However, the effectiveness of these methods heavily depends on the observed data and the accuracy of the propensity score estimator. For example, inverse propensity…

随机临床试验处理效应估计中的线性模型协变量调整：指导模拟的理论AI 解读

Building on key papers that were published in special issues of Biometrics in 1957 and 1982 we propose and develop a three-aspect system for evaluating the effect of fitting covariates in the analysis of designed experiments, in particular randomised clinical trials. The three aspects are: first the effect on residual mean square error, second the effect on the variance inflation factor (VIF) and third the effect on second order precision. We concentrate, in particular, on…

加权平行趋势下截距平移合成控制估计量的一致性AI 解读

The average treatment effect on the treated (ATT) in a staggered-adoption panel is estimated using an intercept-augmented synthetic-control (SCM) estimator. A weighted parallel trends plus an intercept shift, together with mild regularity on the weight vectors (non-degenerate dispersion) and expanding pre-treatment length, are sufficient for consistency allowing for heavy-tailed shocks. These conditions can be more interpretable than the autoregressive or low-rank factor…

未观测混杂的多元回归分析AI 解读

Whereas confidence intervals are used to assess uncertainty due to unmeasured individuals, confounding intervals can be used to assess uncertainty due to unmeasured attributes. Previously, we have introduced a methodology for computing confounding intervals in a simple regression setting in a paper titled ``Regression Analysis of Unmeasured Confounding." Here we extend that methodology for more general application in the context of multiple regression. Our multiple…

图神经网络的过度压缩与重连策略的因果评估AI 解读

Graph neural networks (GNNs) have exhibited state-of-the-art performance across wide-range of domains such as recommender systems, material design, and drug repurposing. Yet message-passing GNNs suffer from over-squashing -- exponential compression of long-range information from distant nodes -- which limits expressivity. Rewiring techniques can ease this bottleneck; but their practical impacts are unclear due to the lack of a direct empirical over-squashing metric. We…

协变量缺失时协变量自适应随机试验的回归调整AI 解读

Covariate-adaptive randomization is widely used in clinical trials to balance prognostic factors, and regression adjustments are often adopted to further enhance the estimation and inference efficiency. In practice, the covariates may contain missing values. Various methods have been proposed to handle the covariate missing problem under simple randomization. However, the statistical properties of the resulting average treatment effect estimators under stratified…

面板与网络数据中带一般固定效应的二元 Logit 选择模型AI 解读

This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies…

因果效应与反事实的界限估计AI 解读

Causal inference often hinges on strong assumptions - such as no unmeasured confounding or perfect compliance - that are rarely satisfied in practice. Partial identification offers a principled alternative: instead of relying on unverifiable assumptions to estimate causal effects precisely, it derives bounds that reflect the uncertainty inherent in the data. Despite its theoretical appeal, partial identification remains underutilized in applied work, in part due to the…

内生连续处理因果效应的部分识别AI 解读

No unmeasured confounding is a common assumption when reasoning about counterfactual outcomes, but such an assumption may not be plausible in observational studies. Sensitivity analysis is often employed to assess the robustness of causal conclusions to unmeasured confounding, but existing methods are predominantly designed for binary treatments. In this paper, we provide natural extensions of two extensively used sensitivity frameworks -- the Rosenbaum and Marginal…

存在小群组时的 K-means 面板数据聚类AI 解读

We consider panel data models with group structure. We study the asymptotic behavior of least-squares estimators and information criterion for the number of groups, allowing for the presence of small groups that have an asymptotically negligible relative size. Our contributions are threefold. First, we derive sufficient conditions under which the least-squares estimators are consistent and asymptotically normal. One of the conditions implies that a longer sample period is…

用于平均处理效应估计的因子辅助双重深度学习AI 解读

We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have sparse nonlinear effects on the propensity and outcome models. We present the use of a Double Deep Learning strategy for estimation, which involves combining recently developed factor-augmented deep learning-based estimators, FAST-NN, for both the response functions and propensity scores to…

基于匹配的组平均处理效应非参数估计AI 解读

Heterogeneous treatment effects, which vary according to individual covariates, are crucial in fields such as personalized medicine and tailored treatment strategies. In many applications, rather than considering the heterogeneity induced by all covariates, practitioners focus on a few key covariates to develop tailored treatment decisions. Based on this, we aim to estimate the group average treatment effects (GATEs), which represent heterogeneous treatment effects across…

结局误分类与非随机验证子集下整群随机试验的平均处理效应估计AI 解读

Randomized trials are viewed as the benchmark for assessing causal effects of treatments on outcomes of interest. Nonetheless, challenges such as measurement error can undermine the standard causal assumptions for randomized trials. In ASPIRE, a cluster-randomized trial, pediatric primary care clinics were assigned to one of two treatments aimed at promoting clinician delivery of a secure firearm program to parents during well-child visits. A key outcome of interest is thus…

可分离辅助参数下部分识别参数的两阶段推断AI 解读

This paper develops a two-stage method for inference on partially identified parameters in moment inequality models with separable nuisance parameters. In the first stage, the nuisance parameters are estimated separately, and in the second stage, the identified set for the parameters of interest is constructed using a refined chi-squared test with variance correction that accounts for the first-stage estimation error. We establish the asymptotic validity of the proposed…