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The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset.
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. The included libraries are Bnlearn, Pgmpy, CausalNex, DoWhy, and CausalImpact. By the end of this blog, you will have a better understanding of these five causal inference libraries and determine which fits best for your use case.
Background
Causal inference is to determine the cause-and-effect relationships between variables in a process or system. In general, we can separate variables into two distinct groups; driver and passenger variables. Driver variables are those that directly influence the outcome or dependent variable, while passenger variables are those that do not have…
