Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses

Author:

Røysland Kjetil1,C. Ryalen Pål12,Nygård Mari3,Didelez Vanessa45ORCID

Affiliation:

1. Department of Biostatistics, University of Oslo , Oslo , Norway

2. Department of Mathematics, EPFL , Lausanne , Switzerland

3. Department of Research, Cancer Registry of Norway, Norwegian Institute of Public Health , Oslo , Norway

4. Department of Mathematics and Computer Science, University of Bremen , Bremen , Germany

5. Leibniz Institute for Prevention Research and Epidemiology – BIPS , Achterstr 30, D-28359 Bremen , Germany

Abstract

Abstract We consider continuous-time survival and event-history settings, where our aim is to graphically represent causal structures allowing us to characterize when a causal parameter is identified from observational data. This causal parameter is formalized as the effect on an outcome event of a (possibly hypothetical) intervention on the intensity of a treatment process. To establish identifiability, we propose novel graphical rules indicating whether the observed information is sufficient to obtain the desired causal effect by suitable reweighting. This requires a different type of graph than in discrete time. We formally define causal semantics for the corresponding dynamic graphs that represent local independence models for multivariate counting processes. Importantly, our work highlights that causal inference from censored data relies on subtle structural assumptions on the censoring process beyond independent censoring; these can be verified graphically. Put together, our results are the first to establish graphical rules for nonparametric causal identifiability in event processes in this generality for the continuous-time case, not relying on particular parametric survival models. We conclude with a data example on Human papillomavirus (HPV) testing for cervical cancer screening, where the assumptions are illustrated graphically and the desired effect is estimated by reweighted cumulative incidence curves.

Publisher

Oxford University Press (OUP)

Reference64 articles.

1. Survival and Event History Analysis

2. Causality, mediation and time: A dynamic viewpoint;Aalen;Journal of the Royal Statistical Society: Series A,2012

3. Dynamic modelling and causality;Aalen;Scandinavian Actuarial Journal,1987

4. Statistical Models Based on Counting Processes

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3