Causal AI — Enabling Data-Driven decisions

Despite the progress made by AI in recent years. the models are not yet capable of understanding the context necessary to interpret the results, and therefore to make decisions.

The consequences of a decision can have serious consequences. For this, it is essential to know the reason behind decision-making in order to improve each decision.

To achieve this, new approaches to automatic learning based on the use of causal reasoning are needed. The principle of operation is as follows: causal inference estimates the causal effect of an intervention on certain outcomes from real-world observational data, holding all other variables constant.