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

By O. Aalen, K. Røysland, J. M. Gran, R. Kouyos, Theis Lange. 2014

80 citations recorded by the source corpus.

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.

Subfields: History and Philosophy of Science, Statistics and Probability

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, methodology): “…models, techniques such as Bayesian Program Learning (Lake et al., 2015), learning models of causal relationships (Maier et al., 2010, 2013; Aalen et al., 2016), and stochastic grammars to learn more inter-pretable structures (Brendel and Todorovic, 2011; Zhou and De la Torre, 2012; Park…”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “There are also assumptions and phenomena that current causal diagrams leave implicit or do not capture (Dawid 2008; Flanders et al. 2011; Maldonado 2013; Greenland and Mansournia 2015a; Aalen et al. 2016), such as random confounding, or capture only clumsily, like interactions.”