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高维线性中介模型中间接效应的估计与推断AI 解读

Summary Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect…

广义工具变量不等式：检验工具变量独立性假设AI 解读

Summary This paper proposes a new set of testable implications for the instrumental variable independence assumption for discrete treatment, but unrestricted outcome and instruments: generalized instrumental inequalities. When outcome and treatment are both binary, but instruments are unrestricted, we show that the generalized instrumental inequalities are necessary and sufficient to detect all observable violations of the instrumental variable independence assumption. To…

异质性处理效应模型中的双向排除限制AI 解读

Summary In this paper, we propose a novel method to identify the conditional average treatment effect partial derivative (CATE-PD) in an environment in which the treatment is endogenous, the treatment effect is heterogeneous, the candidate 'instrumental variables' can be correlated with latent errors, and the treatment selection does not need to be (weakly) monotone. We show that CATE-PD is point-identified under mild conditions if two-way exclusion restrictions exist: (a)…

具有多个阈值的回归不连续设计AI 解读

Numerous empirical studies employ regression discontinuity designs with multiple cutoffs and heterogeneous treatments. A common practice is to normalize all the cutoffs to zero and estimate one effect. This procedure identifies the average treatment effect (ATE) on the observed distribution of individuals local to existing cutoffs. However, researchers often want to make inferences on more meaningful ATEs, computed over general counterfactual distributions of individuals,…

用于识别服从者与推广因果效应的锐度工具变量AI 解读

It is well known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify “local” effects among compliers, that is, those subjects who take treatment only when encouraged by the IV. Local effects are controversial since they seem to only apply to an unidentified subgroup; this has led many to denounce these effects as having little policy relevance. However, we show that such pessimism is not always warranted: it can be…

中央清算的价值AI 解读

ABSTRACT I study a contracting innovation that suddenly insulated traders of hedging contracts against counterparty risk: central clearing counterparties (CCPs) for derivatives. The first CCP was created in Le Havre (France) in 1882, in the coffee futures market. Using triple difference‐in‐differences estimation, I show that central clearing changed the geography of trade flows Europe‐wide, to the benefit of Le Havre. Inspecting the mechanism using trader‐level data, I find…

含生成回归变量的半参数模型之似然推断AI 解读

Hahn and Ridder (2013, Econometrica 81, 315–340) formulated influence functions of semiparametric three-step estimators where generated regressors are computed in the first step. This class of estimators covers several important examples for empirical analysis, such as production function estimators by Olley and Pakes (1996, Econometrica 64, 1263–1297) and propensity score matching estimators for treatment effects by Heckman, Ichimura, and Todd (1998, Review of Economic…

用于政策评估的分布合成控制方法AI 解读

Summary We extend the synthetic control method to evaluate the distributional effects of policy intervention in the possible presence of poor matching. The counterfactuals (or intervention effects) are identified by matching a vector of pre‐intervention quantile residuals of the treated unit and a convex combination of its potential‐control counterparts. The residuals are orthogonal to a set of observable common factors that control for the potentially poor matching. We also…

处理评估中的内生性与无应答偏差：借助工具变量非参数识别因果效应AI 解读

Summary This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non‐response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non‐response/attrition, we identify the average treatment effect on compliers as well as the total population under the assumption of additive separability of observed…

求职者就业促进项目的计量评估方法比较AI 解读

Summary We test whether different identification strategies give similar results when evaluating activation programs. Budgetary problems at the Dutch unemployment insurance (UI) administration in March 2010 caused a sharp drop in the availability of these programs. Using administrative data provided by the UI administration, we evaluate the effect of the program (1) exploiting the policy discontinuity as a quasi‐experiment, (2) using dynamic matching assuming conditional…

贝叶斯非参数策略搜索及牙周复诊间隔应用AI 解读

Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing…

潜在序贯可忽略性下序贯处理的贝叶斯推断AI 解读

Federico Ricciardia* , Alessandra Matteib & Fabrizia Mealliba Department of Statistical Science, University College London, London, UK; b Department of Statistics, Computer Science, Applications, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Federico Ricciardi f.ricciardi@ucl.ac.uk Department of Statistical Science, University College London, London, WC1E 7HB,…

协变量自适应随机化程序的统计推断AI 解读

Covariate-adaptive randomization (CAR) procedures are frequently used in comparative studies to increase the covariate balance across treatment groups. However, because randomization inevitably uses the covariate information when forming balanced treatment groups, the validity of classical statistical methods after such randomization is often unclear. In this article, we derive the theoretical properties of statistical methods based on general CAR under the linear model…

简便的局部多项式密度估计量AI 解读

This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require pre-binning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel…

生存结局下最优动态治疗策略的估计AI 解读

Gabrielle Simoneaua* , Erica E. M. Moodiea , Jagtar S. Nijjarb, Robert W. Platta & the Scottish Early Rheumatoid Arthritis Inception Cohort Investigatorsa Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; b Department of Medicine, University of Cambridge, Cambridge, UKColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article…

配对观察性研究中样本平均处理效应的学生化敏感性分析AI 解读

A fundamental limitation of causal inference in observational studies is that perceived evidence for an effect might instead be explained by factors not accounted for in the primary analysis. Methods for assessing the sensitivity of a study’s conclusions to unmeasured confounding have been established under the assumption that the treatment effect is constant across all individuals. In the potential presence of unmeasured confounding, it has been argued that certain patterns…

基于稀疏随机投影的整体定性处理效应检验AI 解读

In contrast to the classical "one size fits all" approach, precision medicine proposes the customization of individualized treatment regimes to account for patients' heterogeneity in response to treatments. Most of existing works in the literature focused on estimating optimal individualized treatment regimes. However, there has been less attention devoted to hypothesis testing regarding the existence of overall qualitative treatment effects, especially when there is a large…

合并多个观察性数据源估计因果效应AI 解读

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data…

模糊回归不连续设计的外部有效性AI 解读

Fuzzy regression discontinuity designs identify the local average treatment effect (LATE) for the subpopulation of compliers, and with forcing variable equal to the threshold. We develop methods that assess the external validity of LATE to other compliance groups at the threshold, and allow for identification away from the threshold. Specifically, we focus on the equality of outcome distributions between treated compliers and always-takers, and between untreated compliers…

双重差分的潜力与陷阱：对《16岁怀孕》及其他应用的反思AI 解读

We use the exchange between Kearney/Levine and Jaeger/Joyce/Kaestner on 16 and Pregnant to reexamine the use of DiD as a response to the failure of nature to properly design an experiment for us. We argue that (1) any DiD paper should address why the original levels of the experimental and control groups differed, and why this would not impact trends, (2) the parallel trends argument requires a justification of the chosen functional form and that the use of the interaction…

因果保序回归AI 解读

Summary In observational studies, potential confounders may distort the causal relationship between an exposure and an outcome. However, under some conditions, a causal dose–response curve can be recovered by using the G-computation formula. Most classical methods for estimating such curves when the exposure is continuous rely on restrictive parametric assumptions, which carry significant risk of model misspecification. Non-parametric estimation in this context is…

利用稀疏线性规划优化两阶段自适应富集随机试验设计AI 解读

Summary Adaptive enrichment designs involve preplanned rules for modifying enrolment criteria based on accruing data in a randomized trial. We focus on designs where the overall population is partitioned into two predefined subpopulations, e.g. based on a biomarker or risk score measured at baseline. The goal is to learn which populations benefit from an experimental treatment. Two critical components of adaptive enrichment designs are the decision rule for modifying…

随机干预的因果中介分析AI 解读

Summary Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be…

高维工具变量回归的统计推断AI 解读

This paper concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a second-stage linear model for the dependent variable, we develop a novel adaptation of the parametric one-step update to a generic second-stage estimator. We provide conditions under which the scaled update is asymptotically normal. We then introduce a…