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Do-Calculus and Causal Interventions: A Practical Guide to Asking “What If” Questions.
You know the past. You can predict what is next, but what happens if we change something in the equation?
Organizations already know what happened last quarter using observational data. With machine learning, we can make predictions of what's coming next. The harder question, and the one that drives decisions, is: what happens if we change something? This is where Do-calculus is used. It tells you what would happen if you reached in and changed something. That is the difference between watching the world and steering it.
In this blog, you will learn the fundamental distinction between observational and interventional inference, why it matters for real-world decision-making. In the second part, you will learn to apply do-calculus using Python’s bnlearn library, complete with hands-on examples. By the end, you will be able to run both observational and interventional queries on your own dataset and understand exactly what makes them different.
If you found this article helpful, you are welcome to follow me because I write more about Bayesian statistics! I recommend experimenting with the hands-on examples in this blog. This…
