bayesian inference in causal models

Bayesian inference in causal models is a statistical approach that combines Bayesian probability theory with causal modeling to estimate the causal relationships between variables. It allows us to quantify the uncertainty associated with the cause-effect relationships in a dataset by integrating prior knowledge and observed data, enabling inference about causality in a more rigorous and explicit manner.

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