Causal inference with Bayesian regression modeling: A tutorial

<p>This tutorial provides a first general introduction to causal inference with the do-calculus, as introduced in the work of Judea Pearl and a hands-on programming example in R for an artificial data set instantiating Simpson's paradox, showcasing the use of Bayesian regression modeling to obtain causal effect estimates with quantified uncertainty.</p>