Ontological Models Supporting Covariates Selection in Observational Studies

Abstract

In the context of causal inference, biostatisticians use causal diagrams to select covariates in order to build multivariate models. These diagrams represent datasets variables and their relations but have some limitations (representing interactions, bidirectional causal relations). The MetBrAYN project aims at building an ontological-based process to tackle these issues. The knowledge acquired by the biostatistician during a methodological consultation for a research question will be represented in a general ontology. In order to aggregate various forms of knowledge the ontology will act as a wrapper. Ontology-based causal diagrams will be semi-automatically built. Founded on inference rules, the global system will help biostatisticians to curate it and to visualize recommended covariates for their research question.

OntoBioStat: Supporting Causal Diagram Design and Analysis

“…Directions for future research include: (i) the implementation of OntoBioStat as an operational system named MetBRaYN [7], combining the strengths of dagitty and OBS with an R interface; (ii) the mapping of ontologies object properties with OntoBioStat causal object properties in order to feed with several instances the ontology.…”

“…distinction between counterfactual probabilistic, sufficient and necessary causes). We designed the OntoBioStat [7] ontology in order to support covariate selection for causal inference. OntoBioStat was built using expert knowledge corpus, theoretical cases, and literature review in order to address several competency questions.…”