Year: 2014
Type: article
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.
Authors: OO Aalen, K Røysland, JM Gran, R Kouyos, T Lange
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,400
Awards: G3073482563, G3255307542, Swiss HIV Cohort Study (SHCS), Swiss HIV Cohort Study (SHCS), G6850529260, G7262634952, Combining molecular epidemiology approaches and mathematical models for the spread of HIV and its coinfections, minimally invasive surgical device for precise application of bioadhesives to prevent membrane leakage and rupture after fetoscopy
OpenAlex ID: W2154227378