Reveal the hidden causal variables in your data set by using the best-suited Bayesian causal inference library: a comparison with hands-on examples of five popular libraries. Understanding the causal effect of variables in systems or processes is very valuable. There are a number of Python libraries that can assist in determining causal relationships. I will compare five popular causal inference libraries in their functionality, ease of use, and flexibility. Each is accompanied by hands-on examples.
