Journal of Causal Inference最新文献

英文中文

Estimating complier average causal effects for clustered RCTs when the treatment affects the service population 当治疗影响到服务人群时，估计聚类随机对照试验的编译器平均因果效应

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0033

Peter Z. Schochet

Abstract Randomized controlled trials (RCTs) sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting (IPW) estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply (reduce) to nonclustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children’s cognitive development scores. An R program for estimation is available for download.

随机对照试验(RCTs)有时会测试旨在改善针对随机化后确定的个体子集的现有服务的干预措施。因此，这种待遇可能影响到服务接受者和所提供服务的构成。由于这种偏差，使用服务接受者和非接受者数据的意向治疗估计可能难以解释。本文使用一种广义估计方程方法来调整IPW权重中估计误差的标准误差，为这些情况下的编译器总体开发因果估计和逆概率加权(IPW)估计器。虽然我们的重点是更一般的聚类随机对照试验，但这些方法也适用于非聚类随机对照试验。仿真结果表明，在假定的识别条件下，估计器达到了名义置信区间覆盖。一项实证应用表明，该方法使用了一项大规模随机对照试验的数据，测试了幼儿服务对儿童认知发展得分的影响。可以下载一个用于估算的R程序。

引用次数: 1

Causal inference in AI education: A primer 人工智能教育中的因果推理:入门

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0048

A. Forney, Scott Mueller

Abstract The study of causal inference has seen recent momentum in machine learning and artificial intelligence (AI), particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability (among others). Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education.

因果推理的研究最近在机器学习和人工智能(AI)领域有了很大的发展势头，特别是在迁移学习、强化学习、自动诊断和可解释性等领域。然而，尽管它越来越多地应用于解决现代人工智能中的许多边界，但大多数人工智能课程中仍然缺乏因果主题。这项工作旨在通过提供课堂准备的介绍来弥合这一差距，这些介绍融入了人工智能的传统主题，为应用于概率和因果推理的新课程和传统课程提供了直观的图形工具，并为教师提供了途径，让他们深刻认识到攀登“因果层次”的优点，以解决关联、干预和反事实推理层面的问题。最后，本研究分享了讲师的经验、成功经验和挑战，并将这些课程整合到多个层次的教育中。

引用次数: 9

Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population. 将随机对照试验对生存结果的治疗效果推广到目标人群的双稳健估计器。

IF 1.7 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 Epub Date: 2022-12-09 DOI: 10.1515/jci-2022-0004

Dasom Lee, Shu Yang, Xiaofei Wang

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 article concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are functionals of treatment-specific survival functions, including differences in survival probability and restricted mean survival times. Motivated by two intuitive but distinct approaches, i.e., imputation based on survival outcome regression and weighting based on inverse probability of sampling, censoring, and treatment assignment, we propose a semiparametric estimator through the guidance of the efficient influence function. The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or the weighting model is correctly specified and is locally efficient when both are correct. In addition, as an alternative to parametric estimation, we employ the nonparametric method of sieves for flexible and robust estimation of the nuisance functions and show that the resulting estimator retains the root-n consistency and efficiency, the so-called rate-double robustness. Simulation studies confirm the theoretical properties of the proposed estimator and show that it outperforms competitors. We apply the proposed method to estimate the effect of adjuvant chemotherapy on survival in patients with early-stage resected non-small cell lung cancer.

在随机对照试验（RCT）参与者与目标人群之间存在异质性的情况下，仅根据 RCT 评估治疗效果往往会导致对真实世界治疗效果的量化存在偏差。为了解决随机对照试验样本估计的治疗效果缺乏普遍性的问题，我们利用了具有目标人群代表性的大样本观察研究。本文涉及评估治疗对目标人群生存结果的影响，并考虑了作为治疗特异性生存函数的一大类估计值，包括生存概率差异和受限平均生存时间。受两种直观但截然不同的方法（即基于生存结果回归的估算和基于抽样、普查和治疗分配的逆概率的加权）的启发，我们通过有效影响函数的指导提出了一种半参数估计器。所提出的估计器具有双重稳健性，即如果生存模型或加权模型中的任何一个指定正确，它对于目标人群估计值都是一致的；如果两个模型都正确，它就是局部有效的。此外，作为参数估计的替代方法，我们还采用了非参数筛分法对骚扰函数进行灵活稳健的估计，并证明所得到的估计值保持了根n一致性和效率，即所谓的率双稳健性。模拟研究证实了所提估计方法的理论特性，并表明它优于竞争对手。我们将提出的方法用于估计辅助化疗对早期切除的非小细胞肺癌患者生存期的影响。

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引用次数: 0

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning 利用随机森林和协作目标学习识别逃避抗体中和的HIV序列

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0053

Yutong Jin, D. Benkeser

Abstract Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of neutralizing many possible strains of the virus. This is particularly challenging in the context of a highly diverse pathogen like HIV. It is therefore of great interest to leverage existing observational data sources to discover antibodies that are able to neutralize HIV viruses via residues where existing antibodies show modest protection. Such information feeds directly into the clinical trial pipeline for monoclonal antibody therapies by providing information on (i) whether and to what extent combinations of antibodies can generate superior protection and (ii) strategies for analyzing past clinical trials to identify in vivo evidence of antibody resistance. These observational data include genetic features of many diverse HIV genetic sequences, as well as in vitro measures of antibody resistance. The statistical learning problem we are interested in is developing statistical methodology that can be used to analyze these data to identify important genetic features that are significantly associated with antibody resistance. This is a challenging problem owing to the high-dimensional and strongly correlated nature of the genetic sequence data. To overcome these challenges, we propose an outcome-adaptive, collaborative targeted minimum loss-based estimation approach using random forests. We demonstrate via simulation that the approach enjoys important statistical benefits over existing approaches in terms of bias, mean squared error, and type I error. We apply the approach to the Compile, Analyze, and Tally Nab Panels database to identify AA positions that are potentially causally related to resistance to neutralization by several different antibodies.

最近的研究表明，被动输注单克隆抗体可以保护个体免受HIV感染。然而，为了使单克隆抗体具有强大的保护作用，抗体必须能够中和许多可能的病毒株。在艾滋病毒等高度多样化的病原体的背景下，这尤其具有挑战性。因此，利用现有的观察数据源来发现能够通过现有抗体显示适度保护的残基来中和艾滋病毒的抗体是非常有兴趣的。这些信息通过提供以下信息，直接输入到单克隆抗体治疗的临床试验管道中:(i)抗体组合是否以及在多大程度上可以产生更好的保护;(ii)分析过去临床试验的策略，以确定体内抗体耐药性的证据。这些观察数据包括许多不同HIV基因序列的遗传特征，以及抗体耐药性的体外测量。我们感兴趣的统计学习问题是开发统计方法，可以用来分析这些数据，以确定与抗体耐药性显著相关的重要遗传特征。由于基因序列数据的高维性和强相关性，这是一个具有挑战性的问题。为了克服这些挑战，我们提出了一种基于随机森林的结果自适应、协作目标最小损失估计方法。我们通过模拟证明，该方法在偏差、均方误差和I型误差方面比现有方法具有重要的统计优势。我们将该方法应用于编译、分析和计数Nab面板数据库，以确定可能与几种不同抗体的中和抗性有因果关系的AA位置。

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引用次数: 0

Bias attenuation results for dichotomization of a continuous confounder 偏置衰减的结果为二分类的连续混杂

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0047

E. Gabriel, J. M. Pena, A. Sjölander

Abstract It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.

摘要二分类方法在估计中会引起偏差和效率损失，这是众所周知的。我们可以很容易地构造一些例子，其中调整二分类混杂因素会导致因果估计中的偏差。文献中还有其他例子，其中调整二分类混杂因素可能比根本不调整更有偏见。信息很清楚，不要一分为二。目前尚不清楚的是，是否存在对二分类混杂因素进行调整总是比不进行调整导致更低偏差的情况。我们提出了几组条件，这些条件表征了人们应该始终调整二分类混杂因素以减少偏差的情况。然后，我们重点介绍了应该更加谨慎地做出调整决定的场景。据我们所知，这是第一次正式提出条件，提供了关于何时应该和可能不应该调整二分类混杂因素的信息。

引用次数: 1

Optimal weighting for estimating generalized average treatment effects 估计广义平均处理效果的最优加权

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0018

Nathan Kallus, Michele Santacatterina

Abstract In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regression modeling, but these may be sensitive to model misspecification, practical violations of positivity, or both. The contribution of this article is twofold. First, we formulate the generalized average treatment effect (GATE) to unify these causal estimands as well as their IPW estimates. Second, we develop a method based on Kernel optimal matching (KOM) to optimally estimate GATE and to find the GATE most easily estimable by KOM, which we term the Kernel optimal weighted average treatment effect. KOM provides uniform control on the conditional mean squared error of a weighted estimator over a class of models while simultaneously controlling for precision. We study its theoretical properties and evaluate its comparative performance in a simulation study. We illustrate the use of KOM for GATE estimation in two case studies: comparing spine surgical interventions and studying the effect of peer support on people living with HIV.

在因果推理中，研究了各种因果效应估计，包括样本、未删减、目标、条件、最优亚群和最优加权平均处理效应。基于逆概率加权(IPW)和结果回归建模，已经为每个估计开发了特别的方法，但这些方法可能对模型错误规范、实际违反正性或两者都很敏感。这篇文章的贡献是双重的。首先，我们制定了广义平均处理效应(GATE)来统一这些因果估计及其IPW估计。其次，我们开发了一种基于核最优匹配(KOM)的方法来最优估计GATE，并找到最容易被KOM估计的GATE，我们称之为核最优加权平均处理效果。KOM在控制精度的同时，对一类模型上加权估计器的条件均方误差提供统一的控制。我们研究了它的理论性质，并在仿真研究中评价了它的比较性能。我们在两个案例研究中说明了KOM对GATE估计的使用:比较脊柱外科干预和研究同伴支持对艾滋病毒感染者的影响。

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引用次数: 5

Decision-theoretic foundations for statistical causality: Response to Shpitser 统计因果关系的决策理论基础:对Shpitser的回应

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0013

P. Dawid

Abstract I thank Ilya Shpitser for his comments on my article, and discuss the use of models with restricted interventions.

我感谢Ilya Shpitser对我的文章的评论，并讨论了限制干预模型的使用。

引用次数: 1

A note on efficient minimum cost adjustment sets in causal graphical models 因果图模型中有效最小成本调整集的注释

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0015

Ezequiel Smucler, A. Rotnitzky

Abstract We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of observable variables. Moreover, we assume that observable variables have positive costs associated with them. We define the cost of an observable adjustment set as the sum of the costs of the variables that comprise it. We show that in this setting there exist adjustment sets that are minimum cost optimal, in the sense that they yield non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that control for observable adjustment sets that have minimum cost. Our results are based on the construction of a special flow network associated with the original causal graph. We show that a minimum cost optimal adjustment set can be found by computing a maximum flow on the network, and then finding the set of vertices that are reachable from the source by augmenting paths. The optimaladj Python package implements the algorithms introduced in this article.

摘要研究了个体化治疗规则下介入均值估计的调整集选择。我们假设一个非参数因果图模型，可能有隐藏变量和至少一个由可观察变量组成的调整集。此外，我们假设可观察变量具有与之相关的正成本。我们将可观察调整集的成本定义为组成该调整集的变量成本的总和。我们表明，在这种情况下，存在最小成本最优的调整集，因为它们产生的干预均值的非参数估计量在那些控制具有最小成本的可观察调整集的调整集中具有最小渐近方差。我们的结果是基于与原始因果图相关联的特殊流网络的构建。我们表明，通过计算网络上的最大流量，然后通过增加路径找到从源可到达的顶点集，可以找到最小成本最优调整集。optimaladj Python包实现了本文中介绍的算法。

引用次数: 4

Treatment effect optimisation in dynamic environments 动态环境下的治疗效果优化

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2020-0009

Jeroen Berrevoets, Sam Verboven, W. Verbeke

Abstract Applying causal methods to fields such as healthcare, marketing, and economics receives increasing interest. In particular, optimising the individual-treatment-effect – often referred to as uplift modelling – has peaked in areas such as precision medicine and targeted advertising. While existing techniques have proven useful in many settings, they suffer vividly in a dynamic environment. To address this issue, we propose a novel optimisation target that is easily incorporated in bandit algorithms. Incorporating this target creates a causal model which we name an uplifted contextual multi-armed bandit. Experiments on real and simulated data show the proposed method to effectively improve upon the state-of-the-art. All our code is made available online at https://github.com/vub-dl/u-cmab.

将因果方法应用于医疗保健、市场营销和经济学等领域受到越来越多的关注。特别是，优化个人治疗效果——通常被称为提升模型——在精准医疗和定向广告等领域已经达到顶峰。虽然现有技术已被证明在许多情况下都是有用的，但它们在动态环境中受到的影响却十分明显。为了解决这个问题，我们提出了一个新的优化目标，很容易纳入强盗算法。把这个目标结合起来，就产生了一个因果模型，我们称之为一个被提升的语境多手强盗。实际数据和仿真数据的实验表明，该方法在现有的基础上得到了有效的改进。我们所有的代码都可以在https://github.com/vub-dl/u-cmab上获得。

引用次数: 5

Causal inference with imperfect instrumental variables 不完全工具变量下的因果推理

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2021-11-04 DOI: 10.1515/jci-2021-0065

N. Miklin, M. Gachechiladze, George Moreno, R. Chaves

Abstract Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations in the scenario. In this article, we establish a quantitative relationship between such violations of instrumental inequalities and the minimal amount of measurement dependence required to explain them for the case of discrete observed variables. As a result, we provide adapted inequalities that are valid in the presence of a relaxed measurement dependence assumption in the instrumental scenario. This allows for the adaptation of existing and new lower bounds on the average causal effect for instrumental scenarios with binary outcomes. Finally, we discuss our findings in the context of quantum mechanics.

工具变量允许在没有干预的情况下对因果关系进行量化。要做到这一点，必须满足一些因果假设，其中最重要的是独立性假设，即工具和任何混淆因素必须是独立的。然而，如果不满足这个独立性条件，我们还能处理不完美的工具变量吗?不完美的工具可以通过违反工具不平等来表现出来，而工具不平等限制了场景中的相关集。在本文中，我们建立了这种违反仪器不等式和最小量的测量依赖之间的定量关系，以解释离散观察变量的情况。因此，我们提供了在仪器场景中存在宽松的测量依赖假设的情况下有效的适应不等式。这允许对具有二元结果的工具情景的平均因果效应的现有和新的下限进行调整。最后，我们在量子力学的背景下讨论我们的发现。

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引用次数: 4

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