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区分个体处理效应与溢出效应的框架AI 解读

This article suggests a causal framework for separating individual-level treatment effects and spillover effects such as general equilibrium, interference, or interaction effects related to treatment distribution. We relax the stable unit treatment value assumption assuming away treatment-dependent interaction between study participants and permit spillover effects within aggregates, for example, regions. Based on our framework, we systematically categorize the…

上游与下游：通用产品代码的扩散及其影响AI 解读

We study the adoption, diffusion, and impacts of the Universal Product Code (UPC) between 1975 and 1992, during the initial years of the bar code system. We find evidence of network effects in the diffusion process. Matched-sample difference-in-differences estimates show that firm size and trademark registrations increase following UPC adoption by manufacturers. Industry-level import penetration also increases with domestic UPC adoption. Our findings suggest that bar codes,…

实验设计中随机化的最优性：极小极大方差与基于设计的推断AI 解读

Abstract I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes vary in a given set and the objective is effect-estimation precision. When this set is permutation symmetric, the optimal design is shown to be complete randomization. Notably, even when the set has structure (i.e., is not permutation symmetric), being minimax-optimal for precision still requires randomization beyond a single partition of units, that is, beyond…

利用工具变量估计最优治疗规则：部分识别学习方法AI 解读

Abstract Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject’s covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is…

锚回归：异质数据与因果性AI 解读

Abstract We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to…

边学边做（LAGO）自适应研究的分析AI 解读

In Learn-As-you-GO (LAGO) adaptive studies, the intervention is a complex multicomponent package, and is adapted in stages during the study based on past outcome data. This design formalizes standard practice in public health intervention studies. An effective intervention package is sought, while minimizing intervention package cost. In LAGO study data, the interventions in later stages depend upon the outcomes in the previous stages, violating standard statistical theory.…

时间一致、非参数、非渐近的置信序列AI 解读

A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon. Our work develops confidence sequences whose widths go to zero, with nonasymptotic coverage guarantees under nonparametric conditions. We draw connections between the Cramér–Chernoff method for exponential concentration, the law of the iterated logarithm (LIL) and the sequential probability ratio test—our confidence sequences are time-uniform extensions of the…

未知干扰下的平均处理效应AI 解读

We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and…

高维协变量观察性研究中的重叠性AI 解读

Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument,…

异方差与多工具变量下的工具变量模型推断AI 解读

This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset of parameters under the assumption that a consistent estimator for the…

回归不连续设计下条件处理效应的单调性检验AI 解读

Summary Researchers are often interested in the relationship between treatment effects and observed individual heterogeneity. This paper proposes the first nonparametric monotonicity test for conditional treatment effects under the popular regression discontinuity framework. The proposed test examines whether the average treatment effect or the local average treatment effect has a monotonic relationship with some of the observed individual characteristics. We show…

潜在结果与M估计量的有限总体推断AI 解读

Summary When a sample is drawn from or coincides with a finite population, the uncertainty of the coefficient estimators is often reported assuming the population is effectively infinite. The recent literature on finite-population inference instead derives an alternative asymptotic variance of the ordinary least squares estimator. Here, I extend the results to the more general setting of M-estimators and also find that the usual robust ‘sandwich’ estimator is conservative.…

异质性因果效应的机器学习估计：经验蒙特卡洛证据AI 解读

Summary We investigate the finite-sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an empirical Monte Carlo study that relies on arguably realistic data generation processes (DGPs) based on actual data in an observational setting. We consider 24 DGPs, eleven causal machine learning estimators, and three aggregation levels of the estimated effects. Four of the considered estimators perform…

具有混合偏差性质的参数刻画AI 解读

Summary We study a class of parameters with the so-called mixed bias property. For parameters with this property, the bias of the semiparametric efficient one-step estimator is equal to the mean of the product of the estimation errors of two nuisance functions. In nonparametric models, parameters with the mixed bias property admit so-called rate doubly robust estimators, i.e., estimators that are consistent and asymptotically normal when one succeeds in estimating both…

回归不连续设计中基于 g 阶序统计量的密度连续性检验AI 解读

In the regression discontinuity design (RDD), it is common practice to assess the credibility of the design by testing the continuity of the density of the running variable at the cut-off, e.g., McCrary (2008). In this paper we propose an approximate sign test for continuity of a density at a point based on the so-called g-order statistics, and study its properties under two complementary asymptotic frameworks. In the first asymptotic framework, the number q of observations…

删除不当行为：BrokerCheck记录的撤销AI 解读

We examine a controversial process, known as expungement, which allows brokers to remove evidence of financial misconduct from public records. From 2007 to 2016, we identify 6660 expungement requests, suggesting that brokers attempt to expunge 12% of the allegations of misconduct reported by customers and firms. When these requests are adjudicated on the merits, arbitrators approve expungement 84% of the time. We show that expungements significantly predict future…

儿童早期发展与父母劳动供给AI 解读

Summary The impact of children's early development status on parental labor market outcomes is not well established in the empirical literature. We combine an instrumental variable approach to account for the endogeneity of the development status with a model of nonrandom labor force participation to identify its impact. A one‐unit increase in our poor child development index reduces long‐term maternal weekly hours worked by 9 h and weekly income by A$215. We provide…

助长冲突？双边援助与冲突的升级和缓和AI 解读

Summary This paper studies the effects of bilateral foreign aid on conflict escalation and deescalation. First, we develop a new ordinal measure capturing the two‐sided and multifaceted nature of conflict. Second, we propose a dynamic ordered probit estimator that allows for unobserved heterogeneity and corrects for endogeneity. Third, we identify the causal effect of foreign aid on conflict by predicting bilateral aid flows based on electoral outcomes of donor countries…

最优治疗决策的一致性与价值信息准则AI 解读

Personalized medicine is a medical procedure that receives considerable scientific and commercial attention. The goal of personalized medicine is to assign the optimal treatment regime for each individual patient, according to his/her personal prognostic information. When there are a large number of pretreatment variables, it is crucial to identify those important variables that are necessary for treatment decision making. In this paper, we study two information criteria:…

处理状态误分类下的工具变量分位数回归AI 解读

Abstract This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics , 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the…

反事实世界中的个体化决策机器智能：作者回复AI 解读

This JASA rejoinder concerns the problem of individualized decision making under point, sign, and partial identification. The paper unifies various classical decision making strategies through a lower bound perspective proposed in Cui and Tchetgen Tchetgen (2020b) in the context of optimal treatment regimes under uncertainty due to unmeasured confounding. Building on this unified framework, the paper also provides a novel minimax solution (i.e., a rule that minimizes the…

利用高维协变量特异性治疗效应曲线估计最优个体化治疗规则AI 解读

With a large number of baseline covariates, we propose a new semiparametric modeling strategy for heterogeneous treatment effect estimation and individualized treatment selection, which are two major goals in personalized medicine. We achieve the first goal through estimating a covariate-specific treatment effect (CSTE) curve modeled as an unknown function of a weighted linear combination of all baseline covariates. The weight or the coefficient for each covariate is…

无直接效应假设下最优策略的有效估计AI 解读

Lin Liua , Zach Shahnb, James M. Robinsc* & Andrea Rotnitzkyda Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC and SJTU-Yale Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China; b IBM Research, Yorktown Heights, NY; c Department of Biostatistics and Epidemiology, Harvard University, Boston, MA; d Department of Economics, Universidad Torcuato Di Tella and CONICET, Buenos Aires, ArgentinaSupplementary materials for…

BAGS：具有亚组特异生存比较的贝叶斯自适应群序贯试验设计AI 解读

Ruitao Lin, Peter F. Thall* & Ying YuanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Peter F. Thall rex@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TA 77030.AbstractA Bayesian group sequential design is proposed that performs survival comparisons within patient…