How can Machine Learning algorithms include better Causality?

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In recent years, machine learning algorithms have known a great success. Thanks to the availability of important amount of data and the increase of computation speed, they have outperformed usual statistical methods.

Nevertheless, as I was learning more about how they work and how to apply them, I came to a surprising fact: most of these algorithms were focusing on making the most accurate predictions or classifications rather than proving cause-and-effect relationships.

And yet, these kind of relationships can be crucial in decision making, especially in the sectors of health, social or behavior sciences.

Consider the following questions:

You can see that these questions are causal questions rather than associative questions. They require not only to prove the cause-effect relationship but to quantify it.

Most of the time, experimental interventions are used: analysts carry out surveys, gather data and analyze it with sophisticated statistical methods. However, these experiments can be costly both in terms of time and money and even raise ethical questions in some cases.

Moreover, when it comes to Machine learning algorithms, they are generally limited for 3 main reasons:

So, is there any other alternative?

In this article, I will share with you my key findings about some important causal modeling tools such as structural models, causal diagrams and their associated logic.

After reading this article, you will learn:

Before getting started, it is essential to revisit the well-known adage: correlation is not causation. It means that you cannot legitimately deduce a cause-and-effect relationship between two variables only because you have observed a correlation between them.

To illustrate this point, let consider the following graph published by Messerli in 2012 in his paper Chocolate Consumption, Cognitive Function, and Nobel Laureates(full paper here).

As pointed out by the author, there is a correlation between a country’s level of chocolate consumption and its population’s cognitive function (r=0.791, p