Xiang Zhou
This letter introduces several doubly, triply, and quadruply robust estimators of the controlled direct effect. Among them, the triply and quadruply robust estimators are locally semiparametric efficient, and well suited to the use of data-adaptive methods for estimating their nuisance functions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
继续探索
We study multiply robust (MR) estimators of the longitudinal g-computation formula of Robins (1986). In the first part of this paper we review and extend the recently proposed parametric multiply robust estimators of Tchetgen-Tchetgen (2009) and Molina, Rotnitzky, Sued and Robins (2017). In the second part of the paper we derive multiply and doubly robust estimators that use non-parametric machine-learning (ML) estimators of nuisance functions in lieu of parametric models. We use sample splitting to avoid the need for Donsker conditions, thereby allowing an analyst to select the ML algorithms …
Multiply-robust Estimator of Cumulative Incidence Function Difference for Right-Censored Competing Risks Data
本文提出一种多重鲁棒估计器，用于估计右删失竞争风险数据中因果特异性累计发病率函数差，通过伪值方法和多重鲁棒框架结合，提高估计鲁棒性。
Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however requiring stringent model assumptions. In this article, we propose double score matching(DSM) for general causal estimands utilizing two balancing scores including the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the de-biasing DSM estimator achieves the multiple robustness property in that it is consistent for the true causal estimand if any model of the propensity…
Item nonresponse is a common issue in surveys. Because unadjusted estimators may be biased in the presence of nonresponse, it is common practice to impute the missing values with the objective of reducing the nonresponse bias as much as possible. However, commonly used imputation procedures may lead to unstable estimators of population totals/means when influential units are present in the set of respondents. In this article, we consider the class of multiply robust imputation procedures that provide some protection against the failure of underlying model assumptions. We develop an efficient v…
This paper develops a multiply robust (MR) dose-response estimator for causal inference problems involving multivalued treatments. We combine a family of generalised propensity score (GPS) models and a family of outcome regression (OR) models to achieve an average potential outcomes estimator that is consistent if just one of the GPS or OR models in each family is correctly specified. We provide proofs and simulations that demonstrate multiple robustness in the context of multivalued causal inference problems.
Doubly protected estimators are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector X of covariates is observed at each individual, and the missing mechanism is assumed to be independent of the response, conditioned on X (missing at random). In recent years, many authors have moved from the mean to the median, and more generally, doubly protected estimators of the quantiles have been proposed, assuming a parametric regression model for the relationship between X and Y a…
When studying treatment effects in multilevel studies, investigators commonly use (semi-)parametric estimators, which make strong parametric assumptions about the outcome, the treatment, and/or the correlation structure between study units in a cluster. We propose a novel estimator of treatment effects that does not make such assumptions. Specifically, the new estimator is shown to be doubly robust, asymptotically Normal, and often more efficient than existing estimators, all without having to make any parametric modeling assumptions about the outcome, the treatment, and the correlation struct…
Multiply robust estimators in longitudinal studies with missing data under control-based imputation
本文在跳转到参照组（J2R）框架下提出纵向研究缺失数据中评估平均治疗效应的新估计量，其中基于有效影响函数的估计量具有多重稳健性，并通过模拟和临床试验验证了其有限样本表现。
Instrumental variables (IVs) are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard IV model, however, the average treatment effect (ATE) is only partially identifiable. To address this, we propose novel assumptions that allow for identification of the ATE. Our identification assumptions are clearly separated from model assumptions needed for estimation, so that researchers are not required to commit to a specific observed data model in establishing identification. We then construct multiple estimators that are consistent under three differe…
Consider a logistic partially linear model, in which the logit of the mean of a binary response is related to a linear function of some covariates and a nonparametric function of other covariates. We derive simple, doubly robust estimators of coefficient for the covariates in the linear component of the partially linear model. Such estimators remain consistent if either a nuisance model is correctly specified for the nonparametric component, or another nuisance model is correctly specified for the means of the covariates of interest given other covariates and the response at a fixed value. In …
Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stratification characterizes such a mechanism by targeting subgroup causal effects within principal strata, which are defined by the joint potential values of an intermediate variable. Due to the fundamental problem of causal inference, principal strata are inherently latent, rendering it challenging to identify and estimate subgroup effects within them. A line of research leverages the principal ignorability assumption …
Estimation of causal parameters from observational data requires complete confounder adjustment, as well as positivity of the propensity score for each treatment arm. There is often a trade-off between these two assumptions: confounding bias may be reduced through adjustment for a large number of pre-treatment covariates, but positivity is less likely in analyses with irrelevant predictors of treatment such as instrumental variables. Under empirical positivity violations, propensity score weights are highly variable, and doubly robust estimators suffer from high variance and large finite sampl…
Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this work we propose nonparametric estimators for the local average treatment effect on survival probabilities under both nonignorable and ignorable censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustnes…
The NPMLE of a distribution function from doubly truncated data was introduced in the seminal paper of Efron and Petrosian. The consistency of the Efron-Petrosian estimator depends however on the assumption of independent truncation. In this work we introduce an extension of the Efron-Petrosian NPMLE when the variable of interest and the truncation variables may be dependent. The proposed estimator is constructed on the basis of a copula function which represents the dependence structure between the variable of interest and the truncation variables. Two different iterative algorithms to comput…
Multiply robust estimation of causal effects using linked data
针对观察性研究中未测量混杂和数据链接引入的选择偏差问题，提出三种非参数识别策略并构建三重稳健估计器，在模型部分正确时保持一致且局部有效。
Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the …
Improve Efficiency of Doubly Robust Estimator when Propensity Score is Misspecified
本文提出倾向评分增广双重稳健（PAD）估计量，通过在PS模型中引入校准协变量并求解受限加权最小二乘问题，在PS模型错误而OR模型正确时显著降低ATT估计方差，同时保持双重稳健性。
Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
针对应用中对双稳健估计的误解与误用问题，本文介绍了IPTW GLM双稳健估计方法，阐释其双稳健性原理，澄清相关认知误区，并提供代码与示例辅助理解应用。
Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings it may be reasonable to assume individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, i.e., there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been proposed of different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modeled. Alternatively, one can es…
Doubly robust augmented weighting estimators for the analysis of externally controlled single-arm trials and unanchored indirect treatment comparisons
本文提出了一种双重鲁棒的增强加权估计器，用于外部控制单臂试验和无锚间接治疗比较，通过结合熵平衡MAIC权重和条件结果模型预测，提高模型鲁棒性和估计精度。
Missing outcome data is one of the principal threats to the validity of treatment effect estimates from randomized trials. The outcome distributions of participants with missing and observed data are often different, which increases the risk of bias. Causal inference methods may aid in reducing the bias and improving efficiency by incorporating baseline variables into the analysis. In particular, doubly robust estimators incorporate estimates of two nuisance parameters: the outcome regression and the missingness mechanism, to adjust for differences in the observed and unobserved groups that ca…
Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials
本文提出了一种适用于个体和集群随机试验的双重稳健估计方法，用于估计受限治疗时间在治疗有利方面的估计量，解决了传统方法在处理集群数据时的局限性。
We consider the estimation of the average treatment effect in the treated as a function of baseline covariates, where there is a valid (conditional) instrument. We describe two doubly robust (DR) estimators: a locally efficient g-estimator, and a targeted minimum loss-based estimator (TMLE). These two DR estimators can be viewed as generalisations of the two-stage least squares (TSLS) method to semi-parametric models that make weaker assumptions. We exploit recent theoretical results that extend to the g-estimator the use of data-adaptive fits for the nuisance parameters. A simulation study is…
Identification and multiply robust estimation in causal mediation analysis across principal strata
该研究针对存在治疗后事件的因果中介分析场景，识别了全人群及各主层的自然中介效应，推导了有效影响函数并提出多重稳健估计量，还开发了非参数有效估计量与敏感性分析方法，经模拟和真实数据验证了方法有效性。
A doubly robust estimator for the Mann Whitney Wilcoxon Rank Sum Test when applied for causal inference in observational studies
针对现有MWWRST双重稳健估计量的各类局限，研究基于函数响应模型（FRM）开发新的双重稳健估计量，并通过模拟与真实数据验证其性能，用于观察性研究的因果推断。
Efficiency-improved doubly robust estimation with non-confounding predictive covariates
本文提出一种利用非混杂预测协变量并允许其缺失的双重稳健估计量，在倾向得分模型正确设定时达到最小渐近方差，并通过模拟和右心导管插入术实例验证其优于现有方法的效率。
Why Is the Double-Robust Estimator for Causal Inference Not Doubly Robust for Variance Estimation?
本文探讨了因果推断中双重稳健估计器在方差估计中的局限性，并提出混合框架方法以实现有效推断。
Doubly robust estimation and sensitivity analysis for marginal structural quantile models
针对边际结构分位数模型，提出双重稳健估计量及混杂函数敏感性分析方法，并通过模拟和电子健康记录数据验证其性能与稳健性。
Smoothing spline density estimation from doubly truncated data
本文提出了一种修正惩罚似然方法，用于从双截断数据中进行平滑样条密度估计，并通过模拟和实际数据验证其有效性。
Doubly Robust Estimators of Quantile Treatment Effects With Semiparametric Cumulative Probability Models
针对现有分位数处理效应（QTE）估计方法对模型误设敏感的问题，提出基于半参数累积概率模型（CPM）的双重稳健QTE估计框架，开发两种估计策略并通过模拟和HIV数据验证其有效性。
When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and nontreated groups. We then integrate all results through imputation, inverse probability weighting and doubly robust augmentation estimators. Doubly robust estimators are locally efficient while imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator to automatically comb…
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This paper concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are …
Doubly Robust Estimators with Weak Overlap
本文提出一种在修剪倾向分数后仍保持双重稳健性的估计方法，通过偏倚校正提升估计精度，在四个应用中展示了更精确的结果。
Standard Monte Carlo computation is widely known to exhibit a canonical square-root convergence speed in terms of sample size. Two recent techniques, one based on control variate and one on importance sampling, both derived from an integration of reproducing kernels and Stein's identity, have been proposed to reduce the error in Monte Carlo computation to supercanonical convergence. This paper presents a more general framework to encompass both techniques that is especially beneficial when the sample generator is biased and noise-corrupted. We show our general estimator, which we call the doub…
Transported mediation effects may contribute to understanding how and why interventions may work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose several different estimators of transported stochastic direct and indirect effects: an inverse-probability of treatment stabilized weighted estimator, a doubly robust estimator that solves the estimating equation, and a doubly robust substitution estimator in the targeted minimum loss-based framework. We demonstrate their finite sample properties in a simulation study.
Missing data and confounding are two problems researchers face in observational studies for comparative effectiveness. Williamson et al. (2012) recently proposed a unified approach to handle both issues concurrently using a multiply-robust (MR) methodology under the assumption that confounders are missing at random. Their approach considers a union of models in which any submodel has a parametric component while the remaining models are unrestricted. We show that while their estimating function is MR in theory, the possibility for multiply robust inference is complicated by the fact that param…
Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper, we consider various sets of assumptions under which causal inference is possible despite missing attributes and discuss corresponding approaches to average treatment effect estimation, including generalized propensity score methods and multiple imputation. Across an extensive simulation study, we show that no single method systematically out-performs others. We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing…
Ensemble Doubly Robust Bayesian Inference via Regression Synthesis
针对倾向得分与结果模型均可能错误设定的问题，本文提出双重稳健贝叶斯回归合成方法，通过个体自适应集成权重提升因果效应估计的稳健性与不确定性量化能力。
Doubly robust integration of nonprobability and probability survey data
提出双重稳健估计器整合非概率样本与概率调查数据，扩展至子域估计，并与仅使用概率数据的估计器组合，给出方差公式和渐近效率分析。
In this review we cover the basics of efficient nonparametric parameter estimation (also called functional estimation), with a focus on parameters that arise in causal inference problems. We review both efficiency bounds (i.e., what is the best possible performance for estimating a given parameter?) and the analysis of particular estimators (i.e., what is this estimator's error, and does it attain the efficiency bound?) under weak assumptions. We emphasize minimax-style efficiency bounds, worked examples, and practical shortcuts for easing derivations. We gloss over most technical details, in …
Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the no…
Demystify Doubly-Robust Estimation: The Role of Overlap
本文研究了双重稳健估计器在有限样本中的性能，发现其表现受协变量重叠影响显著，建议在应用中检查重叠程度并考虑调整目标人群。
Doubly robust estimation and inference for a log-concave counterfactual density
针对含二元/离散处理的观测数据因果推断问题，提出带对数凹形状约束的反事实密度双重鲁棒估计量，证明其一致性并构建逐点置信区间与置信带，为分布差异分析提供工具。
Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference
针对因果推断中平均处理效应估计时难选最优超参数的问题，提出整合多类初始模型的多重鲁棒估计器，证明其一致性与渐近正态性，给出无需真实模型的标准误计算方法，模拟结果验证理论结论。
Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation
本文提出了一种在存在协变量诱导依赖左截断情况下具有双重鲁棒性的估计方法，通过半参数理论构建高效影响曲线，解决了传统方法在处理左截断时的局限性。
Doubly robust causal inference through penalized bias-reduced estimation: combining non-probability samples with designed surveys
针对非概率样本因果推断中的选择偏差与高维协变量挑战，提出一步插入式双重稳健估计量，通过最小化平方渐近偏差构建惩罚估计方程，实现高维环境下的有效推断并验证了双重稳健性。
Doubly Robust Estimation of Causal Excursion Effects in Micro-Randomized Trials with Missing Longitudinal Outcomes
针对微随机试验中纵向结果随机缺失导致因果偏移效应估计偏差的问题，提出两阶段双重稳健估计器，支持多种结果类型与建模方式，在缺失或结果模型任一正确时保持一致性与渐近正态性，并通过模拟和 HeartSteps 验证。
Unmeasured confounding and selection bias are often of concern in observational studies and may invalidate a causal analysis if not appropriately accounted for. Under outcome-dependent sampling, a latent factor that has causal effects on the treatment, outcome, and sample selection process may cause both unmeasured confounding and selection bias, rendering standard causal parameters unidentifiable without additional assumptions. Under an odds ratio model for the treatment effect, Li et al. 2022 established both proximal identification and estimation of causal effects by leveraging a pair of ne…
Propensity weighting plus adjustment in proportional hazards model is not doubly robust
本研究证明在比例风险模型中结合倾向性评分加权与调整无法实现双重稳健估计，仅在无因果效应零假设下一致，并提出两种对生存差异及一种对完整生存曲线的双重稳健替代估计方法。
Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several different estimators for the average causal effect (ACE). The data generating mechanisms for the simulated treatment and outcome included log-transforms, polynomial terms, and discontinuities. We compared singly-robust estimators (g-computation, in…
On Doubly Robust Estimation with Nonignorable Missing Data Using Instrumental Variables
针对非不可忽略缺失数据的结局均值估计问题，提出基于工具变量的半参数双重稳健估计方法，在部分模型误设下仍具一致性与渐近正态性，经模拟验证并应用于HIV血清阳性率评估的缺失数据校正。
Doubly Robust Weighted Regression for Causal Inference under Confounder Missingness
本文提出一种双重稳健的加权普通最小二乘估计方法，用于处理观测研究中缺失混杂变量的问题，通过引入倾向分数模型提升估计的稳健性与准确性。
The doubly robust (DR) estimator, which consists of two nuisance parameters, the conditional mean outcome and the logging policy (the probability of choosing an action), is crucial in causal inference. This paper proposes a DR estimator for dependent samples obtained from adaptive experiments. To obtain an asymptotically normal semiparametric estimator from dependent samples with non-Donsker nuisance estimators, we propose adaptive-fitting as a variant of sample-splitting. We also report an empirical paradox that our proposed DR estimator tends to show better performances compared to other est…
Consider semiparametric estimation where a doubly robust estimating function for a low-dimensional parameter is available, depending on two working models. With high-dimensional data, we develop regularized calibrated estimation as a general method for estimating the parameters in the two working models, such that valid Wald confidence intervals can be obtained for the parameter of interest under suitable sparsity conditions if either of the two working models is correctly specified. We propose a computationally tractable two-step algorithm and provide rigorous theoretical analysis which justi…
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm
针对自然实验处理效应估计在真实非营利数据上结果不一致的问题，本文构建专家指导的合成结果基准，验证双重稳健估计量显著更优，并提出带新损失函数的估计量及可扩展Python工具包。
Doubly-robust inference and optimality in structure-agnostic models with smoothness
本文针对观测数据下的平均处理效应估计问题，提出带光滑性约束的结构不可知混合分布族，推导极小极大下界，设计出双重稳健且在该分布族下达到最优收敛速率的ATE估计量，并通过模拟验证理论结果。
Covariate Balancing and the Equivalence of Weighting and Doubly Robust Estimators of Average Treatment Effects
本文证明在采用特定协变量平衡方法估计倾向得分时，IPW、AIPW和IPWRA估计量对ATE和ATT在数值上等价，并探讨了该等价性对工具变量和双重差分方法的简化意义。
The generalized g-formula can be used to estimate the probability of survival under a sustained treatment strategy. When treatment strategies are deterministic, estimators derived from the so-called efficient influence function (EIF) for the g-formula will be doubly robust to model misspecification. In recent years, several practical applications have motivated estimation of the g-formula under non-deterministic treatment strategies where treatment assignment at each time point depends on the observed treatment process. In this case, EIF-based estimators may or may not be doubly robust. In thi…
This paper investigates the problem of making inference about a parametric model for the regression of an outcome variable $Y$ on covariates $(V,L)$ when data are fused from two separate sources, one which contains information only on $(V, Y)$ while the other contains information only on covariates. This data fusion setting may be viewed as an extreme form of missing data in which the probability of observing complete data $(V,L,Y)$ on any given subject is zero. We have developed a large class of semiparametric estimators, which includes doubly robust estimators, of the regression coefficients…
Doubly robust estimation of optimal treatment regimes for survival data using an instrumental variable
本文针对存在不可观测混杂因素和工具变量的生存数据，提出基于 Kaplan-Meier 型生存函数最大化的半参数最优治疗方案估计方法，并构造 doubly robust 估计量及核平滑改进，通过模拟和实际癌症筛查数据验证其有效性。
扫码添加微信好友，提出您的宝贵建议 👇
💡 备注请填写：网站反馈
发表机构由 AI 从论文文本抽取，如发现错误，欢迎反馈修正建议。