About Causal AI & ML

Artificial Intelligence (AI) and Machine Learning (ML) have become ubiquitous in our everyday lives from consumer applications to enterprise systems. Predictive analytics, a field of machine learning, has gone from a nascent concept over ten years ago in student success to now a critical component to improving outcomes in all areas of education.

Now, Causal AI and ML can help identify precise relationships between cause and effect. It seeks to model the impact of interventions and distribution changes using a combination of data-driven learning that are not part of the statistical description of a system. 

While predictive analytics has “grown-up”, there still remains questions and concerns about its use in education. Specifically, concerns around black-box algorithms, trust in prediction scores, using past data to model the future in an ever changing world of pandemics and demographic shifts in Education. As such, the activity of solely using machine learning to train and test models puts into many questions its ability to truly shed a light on the right key drivers for student success.

Today, Causal AI / ML may offer us a major advance in understanding the cause and effect of student success initiatives and the efficacy of edtech investments, and do it affordably.