Language:

Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

Aalen, OO ; Røysland, K ; Gran, JM ; Kouyos, R ; Lange, T

Statistical methods in medical research, 2016-10, Vol.25 (5), p.2294-2314 [Peer Reviewed Journal]

London, England: SAGE Publications

Full text available

Citations Cited by

Actions
1. Add to e-Shelf
2. Remove from e-Shelf
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms
Author: Aalen, OO ; Røysland, K ; Gran, JM ; Kouyos, R ; Lange, T
Subjects: Bayes Theorem ; Bayesian analysis ; Causal models ; Causality ; Causation ; CD4 Lymphocyte Count ; Cohort analysis ; Cohort Studies ; Computer simulation ; Confounding Factors (Epidemiology) ; Dependence ; Graph theory ; Graphs ; HIV ; HIV Infections - drug therapy ; HIV Infections - epidemiology ; HIV Infections - mortality ; HIV Infections - virology ; Human immunodeficiency virus ; Humans ; Intervals ; Mathematical models ; Medical research ; Modelling ; Models, Statistical ; Regression Analysis ; RNA, Viral ; Switzerland - epidemiology
Is Part Of: Statistical methods in medical research, 2016-10, Vol.25 (5), p.2294-2314
Notes: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Description: 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.
Publisher: London, England: SAGE Publications
Language: English

Links

View this record in MEDLINE/PubMed

Back to results list

Result 1 Next Go to Next page

Implemented by: Social Network Logos:

Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

Aalen, OO ; Røysland, K ; Gran, JM ; Kouyos, R ; Lange, T

London, England: SAGE Publications

Searching Remote Databases, Please Wait