# Causal Inference and Counterfactuals: An Illustration

** Published:**

Causal inference tackles the problem of dealing with causal statements. A rigorous statistical formalism to assess causality has been proposed by Pearl.

Pearlâ€™s framework is based on the definition of *structural equation models*, it introduces the notion of *intervention* and it connects the (statistical, direction-agnostis, equation-based) *observational* regime with the (causal, direction-aware, graph-based) *interventional* regime.

In this notebook, we re-consider an illustrative toy example proposed by Ference Huszar on his blog to explain causal inference in the Pearlian formalism. We re-run the basic simulations for causal inference and we extend the original example in order to illustrate counterfactuals, as well. For a deeper disccussion of the setup, the models and the results, we refer to the original blogpost.