ArticleStatistical methods in medical research2016
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.
Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks.Journal of medical Internet research · 2025Article
- Article
- Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.Statistics in medicine · 2024Article
- Causal inference on human behaviour.Nature human behaviour · 2024Review
- Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses.Cell reports methods · 2023Article
- Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality.Psychometrika · 2022Article
- Dynamic reciprocal relationships between cognitive and functional declines along the Alzheimer's disease continuum in the prospective COGICARE study.Alzheimer's research & therapy · 2021Article
- Reducing socio-economic inequalities in all-cause mortality: a counterfactual mediation approach.International journal of epidemiology · 2020Article
- Continuous-time causal mediation analysis.Statistics in medicine · 2019Article
- Dealing with death when studying disease or physiological marker: the stochastic system approach to causality.Lifetime data analysis · 2019Article
- Use of Causal Diagrams for Nursing Research: a Tool for Application in Epidemiological Studies.Investigacion y educacion en enfermeria · 2019Article
- Bias in matched case-control studies: DAGs are not enough.European journal of epidemiology · 2018Article
- Three controversies in health data science.International journal of data science and analytics · 2018Article
- A DAG-based comparison of interventional effect underestimation between composite endpoint and multi-state analysis in cardiovascular trials.BMC medical research methodology · 2017Article
- For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates.European journal of epidemiology · 2017Article
- Commentary: Incorporating concepts and methods from causal inference into life course epidemiology.International journal of epidemiology · 2016Article
- Caution: work in progress : While the methodological "revolution" deserves in-depth study, clinical researchers and senior epidemiologists should not be disenfranchised.European journal of epidemiology · 2016Article
- Causal inference in multi-state models-sickness absence and work for 1145 participants after work rehabilitation.BMC public health · 2015Article
- Does Cox analysis of a randomized survival study yield a causal treatment effect?Lifetime data analysis · 2015Article
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5 authors.
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Abstract
Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.
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