Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.
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.
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
O O
OO
Aalen
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
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K
Røysland
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
J M
JM
Gran
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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R
Kouyos
Department of Biostatistics, University of Copenhagen, Copenhagen K, Denmark t.lange@biostat.ku.dk.
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T
Lange
D001499:Bayes Theorem; D018791:CD4 Lymphocyte Count; D015984:Causality; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D015658:HIV Infections / Q000188:drug therapy / Q000453:epidemiology* / Q000401:mortality / Q000821:virology; D006801:Humans; D015233:Models, Statistical; D012367:RNA, Viral; D012044:Regression Analysis; D013557:Switzerland / Q000453:epidemiology
D012367:RNA, Viral
causal inference; directed acyclic graphs; mechanisms; modeling