Journal of Causal Inference最新文献

英文中文

Robust inference for matching under rolling enrollment 滚动招生下匹配的鲁棒推理

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-05-02 DOI: 10.1515/jci-2022-0055

Amanda K. Glazer, Samuel D. Pimentel

Abstract Matching in observational studies faces complications when units enroll in treatment on a rolling basis. While each treated unit has a specific time of entry into the study, control units each have many possible comparison, or “pseudo-treatment,” times. Valid inference must account for correlations between repeated measures for a single unit, and researchers must decide how flexibly to match across time and units. We provide three important innovations. First, we introduce a new matched design, GroupMatch with instance replacement, allowing maximum flexibility in control selection. This new design searches over all possible comparison times for each treated-control pairing and is more amenable to analysis than past methods. Second, we propose a block bootstrap approach for inference in matched designs with rolling enrollment and demonstrate that it accounts properly for complex correlations across matched sets in our new design and several other contexts. Third, we develop a falsification test to detect violations of the timepoint agnosticism assumption, which is needed to permit flexible matching across time. We demonstrate the practical value of these tools via simulations and a case study of the impact of short-term injuries on batting performance in major league baseball.

摘要:观察性研究中的匹配在单位滚动入组治疗时面临并发症。虽然每个治疗组都有一个特定的进入研究的时间，但每个控制组都有许多可能的比较时间，或“伪治疗”时间。有效的推断必须考虑到单个单位的重复测量之间的相关性，研究人员必须决定如何灵活地在时间和单位之间进行匹配。我们提供了三个重要的创新。首先，我们引入了一种新的匹配设计，具有实例替换的GroupMatch，允许最大限度地灵活选择控件。这种新设计搜索了每个处理-对照配对的所有可能的比较时间，并且比过去的方法更易于分析。其次，我们提出了一种块引导方法用于滚动入学匹配设计中的推理，并证明它可以正确地解释我们的新设计和其他几个上下文中匹配集之间的复杂相关性。第三，我们开发了一个证伪检验来检测违反时间点不可知论假设的情况，这是允许灵活的跨时间匹配所必需的。我们通过模拟和短期受伤对棒球大联盟打击表现影响的案例研究来证明这些工具的实用价值。

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引用次数: 0

Conditional average treatment effect estimation with marginally constrained models 基于边际约束模型的条件平均治疗效果估计

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-04-29 DOI: 10.1515/jci-2022-0027

W. A. van Amsterdam, R. Ranganath

Abstract Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population. Estimates of the conditional average treatment effect (CATE) are more useful for individualized treatment decision-making, but randomized trials are often too small to estimate the CATE. Examples in medical literature make use of the relative treatment effect (e.g. an odds ratio) reported by randomized trials to estimate the CATE using large observational datasets. One approach to estimating these CATE models is by using the relative treatment effect as an offset, while estimating the covariate-specific untreated risk. We observe that the odds ratios reported in randomized controlled trials are not the odds ratios that are needed in offset models because trials often report the marginal odds ratio. We introduce a constraint or a regularizer to better use marginal odds ratios from randomized controlled trials and find that under the standard observational causal inference assumptions, this approach provides a consistent estimate of the CATE. Next, we show that the offset approach is not valid for CATE estimation in the presence of unobserved confounding. We study if the offset assumption and the marginal constraint lead to better approximations of the CATE relative to the alternative of using the average treatment effect estimate from the randomized trial. We empirically show that when the underlying CATE has sufficient variation, the constraint and offset approaches lead to closer approximations to the CATE.

治疗效果估计通常来自随机对照试验，作为特定患者群体的单一平均治疗效果。条件平均治疗效果(CATE)的估计对个性化治疗决策更有用，但随机试验往往太小而无法估计CATE。医学文献中的例子利用随机试验报告的相对治疗效果(如优势比)，利用大型观察数据集估计CATE。估计这些CATE模型的一种方法是使用相对治疗效果作为抵消，同时估计协变量特异性未经治疗的风险。我们观察到随机对照试验中报告的比值比并不是偏移模型中需要的比值比，因为试验通常报告的是边际比值比。我们引入了一个约束或正则化器来更好地利用随机对照试验的边际优势比，并发现在标准观察性因果推理假设下，该方法提供了一致的CATE估计。接下来，我们证明了在存在未观察到的混淆的情况下，偏移方法对CATE估计无效。我们研究了相对于使用随机试验的平均治疗效果估计的替代方案，偏移假设和边际约束是否能更好地近似CATE。我们的经验表明，当潜在的CATE有足够的变化时，约束和偏移方法导致更接近CATE的近似。

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引用次数: 0

A Lasso approach to covariate selection and average treatment effect estimation for clustered RCTs using design-based methods 基于设计方法的聚类随机对照试验协变量选择和平均治疗效果估计的Lasso方法

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0036

Peter Z. Schochet

Abstract Statistical power is often a concern for clustered randomized control trials (RCTs) due to variance inflation from design effects and the high cost of adding study clusters (such as hospitals, schools, or communities). While covariate pre-specification can improve power for estimating regression-adjusted average treatment effects (ATEs), further precision gains can be achieved through covariate selection once primary outcomes have been collected. This article uses design-based methods underlying clustered RCTs to develop Lasso methods for the post-hoc selection of covariates for ATE estimation that avoids a lack of transparency and model overfitting. Our focus is on two-stage estimators: in the first stage, Lasso estimation is conducted using data on cluster-level averages or sums, and in the second stage, standard ATE estimators are adjusted for covariates using the first-stage Lasso results. We discuss l 1 {l}_{1} consistency of the estimated Lasso coefficients, asymptotic normality of the ATE estimators, and design-based variance estimation. The nonparametric approach applies to continuous, binary, and discrete outcomes. We present simulation results and demonstrate the method using data from a federally funded clustered RCT testing the effects of school-based programs promoting behavioral health.

由于设计效应带来的方差膨胀和增加研究集群(如医院、学校或社区)的高成本，统计能力通常是聚类随机对照试验(rct)关注的问题。虽然协变量预规范可以提高估计回归调整平均治疗效果(ATEs)的能力，但一旦收集了主要结果，就可以通过协变量选择进一步提高精度。本文使用基于设计的聚类随机对照试验方法来开发Lasso方法，用于ATE估计的协变量事后选择，以避免缺乏透明度和模型过拟合。我们的重点是两阶段估计器:在第一阶段，Lasso估计是使用簇水平平均值或总和的数据进行的，在第二阶段，使用第一阶段Lasso结果调整标准ATE估计器的协变量。我们讨论了估计Lasso系数的1 {l}_{1}一致性、ATE估计量的渐近正态性和基于设计的方差估计。非参数方法适用于连续、二值和离散结果。我们展示了模拟结果，并使用联邦资助的聚类随机对照试验的数据展示了该方法，该试验测试了以学校为基础的项目促进行为健康的效果。

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引用次数: 0

Decomposition of the total effect for two mediators: A natural mediated interaction effect framework. 两个中介的总效应分解:一个自然中介的相互作用效应框架。

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2020-0017

Xin Gao, Li Li, Li Luo

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.

中介分析已在许多学科中使用，通过包含中介来解释暴露变量和结果变量之间观察到的关系的机制或过程。在过去十年中，暴露变量的总效应(TE)分解为表征中介途径和相互作用的效应已获得越来越多的兴趣。在这项工作中，我们为两个中介是因果顺序或非顺序的场景开发了分解。目前这一领域的发展主要集中在没有相互作用成分的分解或有相互作用但假设介质之间没有因果顺序的分解。我们提出了一个新的概念，称为自然介导的相互作用(MI)效应，它捕获了两种情况下的双向和三向相互作用，并扩展了文献中的双向MI。我们开发了一种统一的方法，将TE分解为仅由于中介，仅由于互动，中介和互动，在反事实框架内既不中介也不互动的影响。最后，我们使用模拟数据将我们提出的分解方法与非顺序双介质场景中的现有方法进行了比较，并使用真实数据分析说明了顺序双介质场景的拟议分解方法。

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引用次数: 2

Sensitivity analysis for causal effects with generalized linear models 广义线性模型因果效应的敏感性分析

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0040

A. Sjölander, E. Gabriel, I. Ciocănea-Teodorescu

Abstract Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed to quantify deviation from conditional exchangeability, given measured confounders. These sensitivity parameters are combined with the observed data to produce a “bias-corrected” estimate of the causal effect of interest. We provide important generalizations of these sensitivity analyses, by allowing for arbitrary exposures and a wide range of different causal effect measures, through the specification of the target causal effect as a parameter in a generalized linear model with the arbitrary link function. We show how our generalized sensitivity analysis can be easily implemented with standard software, and how its sensitivity parameters can be calibrated against measured confounders. We demonstrate our sensitivity analysis with an application to publicly available data from a cohort study of behavior patterns and coronary heart disease.

残留混杂是观察性研究中常见的偏倚来源。在本文中，我们建立在Brumback等人和Chiba开发的一系列残余混杂的敏感性分析方法的基础上，这些方法的敏感性参数被构建为量化给定测量混杂因素的条件可交换性偏差。这些敏感性参数与观察到的数据相结合，产生对感兴趣的因果效应的“偏差校正”估计。通过在具有任意链接函数的广义线性模型中指定目标因果效应作为参数，通过允许任意暴露和广泛的不同因果效应测量，我们提供了这些敏感性分析的重要概括。我们展示了如何使用标准软件轻松实现广义灵敏度分析，以及如何根据测量的混杂因素校准其灵敏度参数。我们通过对行为模式和冠心病的队列研究的公开可用数据的应用来证明我们的敏感性分析。

引用次数: 0

Decision-theoretic foundations for statistical causality: Response to Pearl 统计因果关系的决策理论基础:对Pearl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-2022-0056

P. Dawid

Abstract I thank Judea Pearl for his discussion of my paper and respond to the points he raises. In particular, his attachment to unaugmented directed acyclic graphs has led to a misapprehension of my own proposals. I also discuss the possibilities for developing a non-manipulative understanding of causality.

我感谢Judea Pearl对我论文的讨论，并对他提出的观点做出回应。特别是，他对无增广有向无环图的依恋导致了对我自己的建议的误解。我还讨论了发展对因果关系的非操纵性理解的可能性。

引用次数: 2

Comment on: “Decision-theoretic foundations for statistical causality” 评析:《统计因果关系的决策理论基础》

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0056

I. Shpitser

引用次数: 1

Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality” 因果关系与决策:戴维的“统计因果关系的决策理论基础”

IF 1.4 4区医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Causal Inference

Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0046

J. Pearl

Abstract In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic Graphs (DAGs)). This editorial compares the methodological features of the two frameworks as well as their epistemological basis.

在最近一期的本刊中，Philip david(2021)提出了一个基于统计决策理论的因果推理框架，即在许多方面与熟悉的因果图框架(例如，有向无环图(dag))兼容。这篇社论比较了这两种框架的方法论特点及其认识论基础。

引用次数: 4

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

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

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Journal of Causal Inference

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