With the rise of large language models (LLMs), it has become accessible for a broader audience to analyze your own data set and, so to speak, “ask questions”. Although this is great, such an approach has also disadvantages when using it as an analytical step in automated pipelines. This is especially the case when the outcome of models can have a significant impact. To maintain control and ensure results are accurate we can also use Bayesian inferences to talk to our data set. In this blog, we will go through the steps on how to learn a Bayesian model and apply do-calculus on the data science salary data set. I will demonstrate how to create a model that allows you to “ask questions” to your data set and maintain control. You will be surprised by the ease of creating such a model using the bnlearn library.
