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未观测混杂下双变量因果发现的双重稳健识别AI 解读

Summary Learning causal relationships between pairs of complex traits from observational studies is of great interest in many scientific fields. However, most existing methods assume the absence of unmeasured confounding and restrict causal relationships between two traits to be unidirectional, assumptions that may be violated in real-world systems. In this paper, we address the problem of bivariate causal discovery in the presence of unmeasured confounding and potential…

协变量分布迁移下的自动去偏机器学习AI 解读

Summary We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment that modifies surrogate covariates used to predict long-term outcomes. Another example is the average treatment effect for a population with a shifted covariate distribution. We…

高维暴露与高维中介模型的统计推断AI 解读

High-dimensional mediation analysis has gained increasing interest in various fields, particularly in genetic and medical research. Compared with existing works that focus mainly on high-dimensional mediators, this paper advocates a new framework of Partial Regularization-based Inference for Mediation Effects (PRIME) when both exposures and mediators are high-dimensional. Estimated direct and indirect effects are established using a group-wise partially penalized least…

Summary We adapt double machine learning to macroeconomic time series by combining regularized nuisance estimation with reverse cross-fitting. This deterministic scheme exploits time reversibility to use time-reversed auxiliary blocks and, unlike neighbour-deletion designs, avoids buffer blocks, thereby improving sample usage. We derive conditions for asymptotic validity and show in simulations that the estimator performs well in realistic finite samples across the designs…

自适应实验的设计稳定性及其对处理效应估计的影响AI 解读

Abstract We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, the setting we consider is one in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential outcomes framework (Neyman, 1923), we propose and analyse two natural estimators for the average treatment…

随机试验中 Rosenbaum 秩估计量的稳健性与效率：设计视角AI 解读

Summary Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by Rosenbaum (1993) estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum’s estimator is robust…

控制错误发现率与功效的贝叶斯 A/B 测试设计AI 解读

Businesses frequently run online controlled experiments (i.e., A/B tests) to learn about the effect of an intervention on multiple business metrics. To account for multiple hypothesis testing, multiple metrics are commonly aggregated into a single composite measure, losing valuable information, or strict family-wise error rate adjustments are imposed, leading to reduced power. In this paper, we propose an economical framework to design Bayesian A/B tests while controlling…

空间干扰效应的高维检测AI 解读

Modeling interference effects in high-dimensional settings presents significant challenges due to the complexity of underlying dependence structures. Existing approaches often rely on explicit and homogeneous assumptions, limiting their applicability in real-world scenarios. In this paper, we introduce a novel low-rank and sparse treatment effect model that leverages high-dimensional techniques to identify interference structures without restrictive parametric assumptions.…

条件分位数处理效应估计的深度正交学习器AI 解读

In recent years, orthogonal statistical learning has been widely recognized for its ability to reduce sensitivity with respect to nuisance parameters to estimate the target parameter, making it an important tool in causal inference, particularly in the estimation of the conditional average treatment effect (CATE). However, its application on conditional quantile treatment effect (CQTE), which offers a more comprehensive perspective on treatment effects than CATE, has not yet…

面向政策制定者的处理效应同质性非参数检验AI 解读

Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null…

助学金与向上流动：来自哥伦比亚 Ser Pilo Paga 项目的证据AI 解读

We study the impact of financial aid on upward mobility. We estimate the causal impacts of a Colombian policy targeting high-achieving, low-socioeconomic-status students on later-life educational and labor market outcomes. Using regression discontinuity design, difference-in-differences, and population-wide administrative microdata, we find that the policy boosted attendance and completion at high-value-added colleges, particularly in STEM fields. Improved college quality…

时变内生随机系数面板模型的识别与估计AI 解读

This paper proposes a correlated random coefficient linear panel data model, where regressors can be correlated with time-varying and individual-specific random coefficients through both a fixed effect and a time-varying random shock. I develop a new panel data-based method to identify the average partial effect and the local average response function. The identification strategy employs a sufficient statistic to control for the fixed effect and a control variable for the…

边界断点设计的估计与推断：基于距离的方法AI 解读

We study nonparametric distance-based (isotropic) local polynomial methods for estimating the boundary average treatment effect curve, a causal functional that captures treatment effect heterogeneity in boundary discontinuity designs. We establish identification, estimation, and inference results both pointwise and uniformly along the treatment assignment boundary. We show that the geometric regularity of the boundary, a one-dimensional manifold, plays a central role in…

未知形式异方差下非基本 ARMA 模型的时域估计AI 解读

This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations…

通过有限跨观测信息汇集界定处理效应AI 解读

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as…

工具变量回归中弱工具稳健的子向量推断：拉格朗日乘子检验与 Anderson–Rubin 置信集AI 解读

We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the…

具有定向作用的工具变量与处理效应AI 解读

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We explore the additional identifying power of a positive…

金融素养与金融犯罪：回归不连续设计AI 解读

This study investigates how financial literacy shapes the propensity of individuals to commit financial crime. Using state-level administrative data on criminal charges linked to comprehensive public records , we exploit a policy-based discontinuity in grade level assignment based on individual birth dates that exogenously requires certain high school cohorts to attend a financial literacy course. Our estimates suggest that exposure to the course reduces the propensity to…

估计分位数最优处理规则的序贯分类学习AI 解读

Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients’ outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it improves outcomes for vulnerable patients. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a successive classification task, solvable via training a sequence of…

存在隐藏混杂时过参数化模型的在线推断AI 解读

Abstract In this paper, we study online estimation and inference of regression coefficients in the presence of hidden confounders by leveraging over-parameterized models. Unlike existing offline approaches that rely on factor and sparse models, our closed-form estimator simultaneously removes hidden-confounder bias and is directly applicable to streaming data. Using tools from random matrix theory, we analyse phase transition phenomena in the variance of the coefficient…

混杂变量非可忽略缺失下中介分析的有效非参数推断AI 解读

Mediation analysis is widely used for exploring treatment mechanisms; however, it faces challenges when nonignorable missing confounders are present. Efficient inference of mediation effects and the efficiency loss due to nonignorable missingness have been rarely studied in the literature because of the difficulties arising from the ill-posed inverse problem. In this paper, we propose a general shadow variable framework for identifying mediation effects, allowing shadow…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

未观测混杂下预测算法的稳健设计与评估AI 解读

Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected…

作为组织问题的国家能力AI 解读

Abstract We investigate how technologies that reduce the costs of monitoring by central authorities have shaped the historical transition from small patrimonial states to large bureaucratic organizations. Our analysis is based on a novel dataset that traces changes in the organizational structure and geographic presence of the U.S. federal government over the 19th century. To identify causal effects, we develop a new identification strategy that exploits the expansion of the…