Balabanov O.S.
Logic of causal inference from data under presence of latent confounders

The problems of causal inference of models from empirical data (by independence-based methods) and some error mechanisms are examined. We demonstrate that the known rules for orienting edges of model can produce misleading results under presence of latent confounders. We propose corrections to the orientation rules aiming to successfully extend them for inference of models beyond the ancestral model class. The necessary assumptions justifying the inference of adequate causal relationships from data are suggested. © 2022, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: causal network, collider, conditional independence, confounder, d-separation, dependence testability assumptions, edge orientation rules, illusory edge, Causal inferences, Causal network, Conditional independences, Confounder, D-separation, Dependence testability assumption, Edge orientation rule, Edge orientations, Illusory edge, Testability

Cite:
Balabanov O.S. (2022). Logic of causal inference from data under presence of latent confounders. Cybernetics and Systems Analysis, 58 (2), 10–28. doi: https://doi.org/10.1007/s10559-022-00448-z http://jnas.nbuv.gov.ua/article/UJRN-0001313057 [In Ukrainian].