Causal Academy is a platform focused entirely on education in causal inference. It is an initiative by CausAI, founded by Jamilla Cooiman, which has been dedicated to causal inference education since early 2024.
The platform was created to address a gap within the data industry: a lack of causal inference skills among data professionals. This gap arises for at least two reasons.
First, many educational programmes emphasize correlation-based modelling approaches, while causal inference receives more limited attention.
Second, causal inference material can be dense and fragmented, making it time-consuming to navigate independently for professionals working in business environments.
As a result, many analysts and data scientists lack formal training in causal analysis, even though causal inference is fundamental to using data to support decision-making. This often leads to analytical solutions being applied to questions they are not designed to answer, which can limit the extent to which data science truly supports business decisions.
In parallel, the causal inference field itself has developed a lot in recent years, with continued advances in estimation methods, software tooling in Python and R, and practical strategies for dealing with the challenges of applied causal work.
Awareness of these developments is increasing, as is the demand for professionals who are able to apply causal methods in applied settings.
Causal Academy was established to support this development by providing structured courses, in-company trainings, and technical content focused on causal inference.
To date, this has resulted in the development of more than 50 hours of course material and has reached over 1,500 learners globally through online education and in-company training.
If you would like to learn more about the founder, you can connect with Jamilla Cooiman on LinkedIn here: