Journal of Causal Inference最新文献_第3页

Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias 因果分解分析的敏感性分析:对遗漏变量偏差的稳健性评估

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-05-26 DOI: 10.1515/jci-2022-0031

S. Park, Suyeon Kang, Chioun Lee, Shujie Ma

{"title":"Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias","authors":"S. Park, Suyeon Kang, Chioun Lee, Shujie Ma","doi":"10.1515/jci-2022-0031","DOIUrl":"https://doi.org/10.1515/jci-2022-0031","url":null,"abstract":"Abstract A key objective of decomposition analysis is to identify a factor (the “mediator”) contributing to disparities in an outcome between social groups. In decomposition analysis, a scholarly interest often centers on estimating how much the disparity (e.g., health disparities between Black women and White men) would be reduced/remain if we set the mediator (e.g., education) distribution of one social group equal to another. However, causally identifying disparity reduction and remaining depends on the no omitted mediator–outcome confounding assumption, which is not empirically testable. Therefore, we propose a set of sensitivity analyses to assess the robustness of disparity reduction to possible unobserved confounding. We derived general bias formulas for disparity reduction, which can be used beyond a particular statistical model and do not require any functional assumptions. Moreover, the same bias formulas apply with unobserved confounding measured before and after the group status. On the basis of the formulas, we provide sensitivity analysis techniques based on regression coefficients and R 2 {R}^{2} values by extending the existing approaches. The R 2 {R}^{2} -based sensitivity analysis offers a straightforward interpretation of sensitivity parameters and a standard way to report the robustness of research findings. Although we introduce sensitivity analysis techniques in the context of decomposition analysis, they can be utilized in any mediation setting based on interventional indirect effects when the exposure is randomized (or conditionally ignorable given covariates).","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72716650","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: 1

Matched design for marginal causal effect on restricted mean survival time in observational studies 观察性研究中限定平均生存时间边际因果效应的匹配设计

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-05-04 DOI: 10.1515/jci-2022-0035

Zihan Lin, A. Ni, Bo Lu

{"title":"Matched design for marginal causal effect on restricted mean survival time in observational studies","authors":"Zihan Lin, A. Ni, Bo Lu","doi":"10.1515/jci-2022-0035","DOIUrl":"https://doi.org/10.1515/jci-2022-0035","url":null,"abstract":"Abstract Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"29 12 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82731058","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

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

Journal of Causal Inference Pub Date : 2022-05-02 DOI: 10.1515/jci-2022-0055

Amanda K. Glazer, Samuel D. Pimentel

{"title":"Robust inference for matching under rolling enrollment","authors":"Amanda K. Glazer, Samuel D. Pimentel","doi":"10.1515/jci-2022-0055","DOIUrl":"https://doi.org/10.1515/jci-2022-0055","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"4 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79025660","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

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

Journal of Causal Inference Pub Date : 2022-04-29 DOI: 10.1515/jci-2022-0027

W. A. van Amsterdam, R. Ranganath

{"title":"Conditional average treatment effect estimation with marginally constrained models","authors":"W. A. van Amsterdam, R. Ranganath","doi":"10.1515/jci-2022-0027","DOIUrl":"https://doi.org/10.1515/jci-2022-0027","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"19 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90261544","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

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2021-0036

Peter Z. Schochet

{"title":"A Lasso approach to covariate selection and average treatment effect estimation for clustered RCTs using design-based methods","authors":"Peter Z. Schochet","doi":"10.1515/jci-2021-0036","DOIUrl":"https://doi.org/10.1515/jci-2021-0036","url":null,"abstract":"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.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"9 1","pages":"494 - 514"},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84301133","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

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0056

P. Dawid

引用次数: 2

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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2020-0017

Xin Gao, Li Li, Li Luo

{"title":"Decomposition of the total effect for two mediators: A natural mediated interaction effect framework.","authors":"Xin Gao, Li Li, Li Luo","doi":"10.1515/jci-2020-0017","DOIUrl":"https://doi.org/10.1515/jci-2020-0017","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"10 1","pages":"18-44"},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10600650","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: 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

Journal of Causal Inference Pub Date : 2022-01-01 DOI: 10.1515/jci-2022-0040

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

引用次数: 0

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

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”