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Distribution-Free Prediction Intervals Under Covariate Shift, With an Application to Causal Inference

协变量分布迁移下的分布无关预测区间及因果推断应用

Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these…

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Tuning-parameter-free propensity score matching approach for causal inference under shape restriction

形状约束下无需调参的倾向得分匹配因果推断

Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM approach to causal inference based on the nonparametric…

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From Conditional Quantile Regression to Marginal Quantile Estimation with Applications to Missing Data and Causal Inference

从条件分位数回归到边际分位数估计:缺失数据与因果推断应用

It is well known that information on the conditional distribution of an outcome variable given covariates can be used to obtain an enhanced estimate of the marginal outcome distribution. This can be done easily by integrating out the marginal covariate distribution from the conditional outcome distribution. However, to date, no analogy has been established between marginal quantile and conditional quantile regression. This article provides a link between them. We propose two…

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Robust and efficient estimation for the treatment effect in causal inference and missing data problems

因果推断与缺失数据问题中处理效应的稳健高效估计

The Mann–Whitney statistic based on complete data only might be invalid if the outcome variables are missing due to certain covariates. In this paper, we used the probabilistic index modelling (Thas et al., 2012) to obtain a new Mann–Whitney statistic when missingness occurs in the outcomes but multiple explanatory variables are observable. Our method combined the efficiency of the model-based approach and the robustness of the nonparametric approach. It requires few model…

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Mann–Whitney Test with Adjustments to Pretreatment Variables for Missing Values and Observational Study

针对缺失值与观察性研究进行处理前变量调整的Mann–Whitney检验

Summary The conventional Wilcoxon or Mann–Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcomes or the participation in the treatments may depend on certain pretreatment variables. We propose an approach to adjust the Mann–Whitney test by correcting the potential bias via consistently estimating the conditional distributions of the outcomes given the…

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Semiparametric Estimation and Inference for Distributional and General Treatment Effects

分布性及一般处理效应的半参数估计与推断

Summary There is a large literature on methods of analysis for randomized trials with noncompliance which focuses on the effect of treatment on the average outcome. The paper considers evaluating the effect of treatment on the entire distribution and general functions of this effect. For distributional treatment effects, fully non-parametric and fully parametric approaches have been proposed. The fully non-parametric approach could be inefficient but the fully parametric…

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Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

前测后测研究中基于经验似然的处理效应估计

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that…

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Empirical-Likelihood-Based Difference-in-Differences Estimators

基于经验似然的双重差分估计量

Summary Recently there has been a surge in econometric and epidemiologic works focusing on estimating average treatment effects under various sets of assumptions. Estimation of average treatment effects in observational studies often requires adjustment for differences in pretreatment variables. Rosenbaum and Rubin have proposed the propensity score method for estimating the average treatment effect by adjusting pretreatment variables. In this paper, the empirical likelihood…

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Inference of Tamoxifen's Effects on Prevention of Breast Cancer From a Randomized Controlled Trial

随机对照试验中他莫昔芬预防乳腺癌效应的推断

The largest randomized, double-blind, placebo-controlled chemoprevention trial, the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (NSABP-BCPT), evaluated the efficacy of tamoxifen in the prevention of breast cancer among women at high risk of developing the disease. The trial has reported a reduction of breast cancer incidence for the tamoxifen group. However, the effect of tamoxifen on the time to diagnosis of the disease over the…

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Empirical-Likelihood-Based Inference in Missing Response Problems and Its Application in Observational Studies

缺失响应问题中的经验似然推断及其观察性研究应用

Summary The problem of missing response data is ubiquitous in medical and social science studies. In the case of responses that are missing at random (depending on some covariate information), analyses focused only on the complete data may lead to biased results. Various debias methods have been extensively studied in the literature, particularly the weighting method that was motivated by Horvitz and Thompson's estimators. To improve efficiency, Robins, Rotnitzky and Zhao…

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