Original article can be found here (source): Artificial Intelligence on Medium

# On the Difficulty of Finding Structural Causal Models

## Research in causality is gaining popularity, but research in causality is difficult! Let’s take an intuitive look at why, and can it be useful for machine learning?

The link between correlation and causation has been discussed in science for quite a bit. Alas, we are all quite certain that correlation can be misleading. Hence the scientific community gave birth to** causal inference and causal discovery**. Previously I wrote an article on statistics versus causality, which is a bit more general than this one. In this article, I want to make it clear what exactly makes it so difficult to find causal models, i**n the context of graphical models, **and yes, it is difficult.

Firstly, to understand the power of **causal models**, consider graphical models in general for describing probability distributions. Bayesian networks are such models (coincidentally introduced by the same person that introduced do calculus, Judea Pearl). Ialson the case of Bayesian networks, we want to model a probability distribution in a smart way. More concretely, we want to model a joint probability distribution. But not just in any way, we want to do it in a smart way, **such that its computational overhead is minimal.** Another way to put it, we want a **sparse factorization **of the joint probability distribution.

To be clearer, let’s write it down in simple terms. Let us assume we have **n** random variables, so the joint probability distribution that we are interested in is