Journal of Causal Inference最新文献_第4页

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0033

Peter Z. Schochet

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0048

A. Forney, Scott Mueller

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

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

{"title":"Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population.","authors":"Dasom Lee, Shu Yang, Xiaofei Wang","doi":"10.1515/jci-2022-0004","DOIUrl":"10.1515/jci-2022-0004","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"10 1","pages":"415-440"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10119183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0053

Yutong Jin, D. Benkeser

{"title":"Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning","authors":"Yutong Jin, D. Benkeser","doi":"10.1515/jci-2021-0053","DOIUrl":"https://doi.org/10.1515/jci-2021-0053","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"43 1","pages":"280 - 295"},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73879080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0047

E. Gabriel, J. M. Pena, A. Sjölander

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0018

Nathan Kallus, Michele Santacatterina

{"title":"Optimal weighting for estimating generalized average treatment effects","authors":"Nathan Kallus, Michele Santacatterina","doi":"10.1515/jci-2021-0018","DOIUrl":"https://doi.org/10.1515/jci-2021-0018","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"126 1","pages":"123 - 140"},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87728464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0013

P. Dawid

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0015

Ezequiel Smucler, A. Rotnitzky

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2020-0009

Jeroen Berrevoets, Sam Verboven, W. Verbeke

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

Journal of Causal Inference Pub Date : 2021-11-04 DOI: 10.1515/jci-2021-0065

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

{"title":"Causal inference with imperfect instrumental variables","authors":"N. Miklin, M. Gachechiladze, George Moreno, R. Chaves","doi":"10.1515/jci-2021-0065","DOIUrl":"https://doi.org/10.1515/jci-2021-0065","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"232 1","pages":"45 - 63"},"PeriodicalIF":1.4,"publicationDate":"2021-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76562151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4