Journal of Causal Inference最新文献_第2页

Randomization-based, Bayesian inference of causal effects 基于随机的贝叶斯因果推理

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2022-0025

Thomas C. Leavitt

引用次数: 0

Bounding the probabilities of benefit and harm through sensitivity parameters and proxies 通过敏感性参数和代理来确定收益和损害的概率

4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2023-0012

Jose M. Peña

引用次数: 0

Model-based regression adjustment with model-free covariates for network interference 基于模型的无模型协变量网络干扰回归平差

4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2023-0005

Kevin Han, Johan Ugander

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引用次数: 1

Attributable fraction and related measures: Conceptual relations in the counterfactual framework 归因分数与相关测度:反事实框架中的概念关系

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2021-0068

E. Suzuki, E. Yamamoto

{"title":"Attributable fraction and related measures: Conceptual relations in the counterfactual framework","authors":"E. Suzuki, E. Yamamoto","doi":"10.1515/jci-2021-0068","DOIUrl":"https://doi.org/10.1515/jci-2021-0068","url":null,"abstract":"Abstract The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this article, we discuss the concepts of and calculation methods for the attributable fraction and related measures in the counterfactual framework, both with and without stratification by covariates. Generally, the attributable fraction is useful when the exposure of interest has a causal effect on the outcome. However, it is important to understand that this statement applies to the exposed group. Although the target population of the attributable fraction (population) is the total population, the causal effect should be present not in the total population but in the exposed group. As related measures, we discuss the preventable fraction and prevented fraction, which are generally useful when the exposure of interest has a preventive effect on the outcome, and we further propose a new measure called the attributed fraction. We also discuss the causal and preventive excess fractions, and provide notes on vaccine efficacy. Finally, we discuss the relations between the aforementioned six measures and six possible patterns using a conceptual schema.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"41 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77940445","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

2D score-based estimation of heterogeneous treatment effects 异质性治疗效果的二维评分估计

4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2022-0016

Steven Siwei Ye, Yanzhen Chen, Oscar Hernan Madrid Padilla

{"title":"2D score-based estimation of heterogeneous treatment effects","authors":"Steven Siwei Ye, Yanzhen Chen, Oscar Hernan Madrid Padilla","doi":"10.1515/jci-2022-0016","DOIUrl":"https://doi.org/10.1515/jci-2022-0016","url":null,"abstract":"Abstract Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment effects, especially in the case when there are a large number of features in estimation. To make efforts to address the issue, we propose a score-based framework for estimating the conditional average treatment effect (CATE) function in this article. The framework integrates two components: (i) leverage the joint use of propensity and prognostic scores in a matching algorithm to obtain a proxy of the heterogeneous treatment effects for each observation and (ii) utilize nonparametric regression trees to construct an estimator for the CATE function conditioning on the two scores. The method naturally stratifies treatment effects into subgroups over a 2d grid whose axis are the propensity and prognostic scores. We conduct benchmark experiments on multiple simulated data and demonstrate clear advantages of the proposed estimator over state-of-the-art methods. We also evaluate empirical performance in real-life settings, using two observational data from a clinical trial and a complex social survey, and interpret policy implications following the numerical results.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135212050","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

Exploiting neighborhood interference with low-order interactions under unit randomized design 单位随机设计下低阶相互作用的邻域干扰研究

4区医学

Journal of Causal Inference Pub Date : 2023-01-01 DOI: 10.1515/jci-2022-0051

Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu

{"title":"Exploiting neighborhood interference with low-order interactions under unit randomized design","authors":"Mayleen Cortez-Rodriguez, Matthew Eichhorn, Christina Lee Yu","doi":"10.1515/jci-2022-0051","DOIUrl":"https://doi.org/10.1515/jci-2022-0051","url":null,"abstract":"Abstract Network interference, where the outcome of an individual is affected by the treatment assignment of those in their social network, is pervasive in real-world settings. However, it poses a challenge to estimating causal effects. We consider the task of estimating the total treatment effect (TTE), or the difference between the average outcomes of the population when everyone is treated versus when no one is, under network interference. Under a Bernoulli randomized design, we provide an unbiased estimator for the TTE when network interference effects are constrained to low-order interactions among neighbors of an individual. We make no assumptions on the graph other than bounded degree, allowing for well-connected networks that may not be easily clustered. We derive a bound on the variance of our estimator and show in simulated experiments that it performs well compared with standard estimators for the TTE. We also derive a minimax lower bound on the mean squared error of our estimator, which suggests that the difficulty of estimation can be characterized by the degree of interactions in the potential outcomes model. We also prove that our estimator is asymptotically normal under boundedness conditions on the network degree and potential outcomes model. Central to our contribution is a new framework for balancing model flexibility and statistical complexity as captured by this low-order interactions structure.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135894033","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

On the pitfalls of Gaussian likelihood scoring for causal discovery 关于因果发现的高斯似然评分的缺陷

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-10-20 DOI: 10.1515/jci-2022-0068

Christoph Schultheiss, P. Bühlmann

引用次数: 1

Quantitative probing: Validating causal models with quantitative domain knowledge 定量探索:用定量领域知识验证因果模型

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-09-07 DOI: 10.1515/jci-2022-0060

Daniel Grünbaum, M. L. Stern, E. Lang

引用次数: 3

Personalized decision making – A conceptual introduction 个性化决策-概念介绍

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-08-19 DOI: 10.48550/arXiv.2208.09558

Scott Mueller

引用次数: 24

Exploiting neighborhood interference with low-order interactions under unit randomized design 单位随机设计下低阶相互作用的邻域干扰研究

IF 1.4 4区医学

Journal of Causal Inference Pub Date : 2022-08-10 DOI: 10.48550/arXiv.2208.05553

Mayleen Cortez, Matthew Eichhorn, C. Yu

{"title":"Exploiting neighborhood interference with low-order interactions under unit randomized design","authors":"Mayleen Cortez, Matthew Eichhorn, C. Yu","doi":"10.48550/arXiv.2208.05553","DOIUrl":"https://doi.org/10.48550/arXiv.2208.05553","url":null,"abstract":"Abstract Network interference, where the outcome of an individual is affected by the treatment assignment of those in their social network, is pervasive in real-world settings. However, it poses a challenge to estimating causal effects. We consider the task of estimating the total treatment effect (TTE), or the difference between the average outcomes of the population when everyone is treated versus when no one is, under network interference. Under a Bernoulli randomized design, we provide an unbiased estimator for the TTE when network interference effects are constrained to low-order interactions among neighbors of an individual. We make no assumptions on the graph other than bounded degree, allowing for well-connected networks that may not be easily clustered. We derive a bound on the variance of our estimator and show in simulated experiments that it performs well compared with standard estimators for the TTE. We also derive a minimax lower bound on the mean squared error of our estimator, which suggests that the difficulty of estimation can be characterized by the degree of interactions in the potential outcomes model. We also prove that our estimator is asymptotically normal under boundedness conditions on the network degree and potential outcomes model. Central to our contribution is a new framework for balancing model flexibility and statistical complexity as captured by this low-order interactions structure.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"52 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73433770","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: 8