Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models.

ABSTRACT:

Background

Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the calculation of structural coefficients has received much attention, a key requirement for valid causal inference is that conclusions are drawn based on the true data-generating model.

Methods

It remains widely unknown how large the probability is to reject the true structural causal model when observational data from it is sampled. The latter probability - the causal false-positive risk - is crucial, as rejection of the true causal model can induce bias in the estimation of causal e

SUBMITTER: Kelter R 

PROVIDER: S-EPMC8883695 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature