  In this competition, participants will address two fundamental causal
challenges in machine learning in the context of education using time-series
data. The first is to identify the causal relationships between different
constructs, where a construct is defined as the smallest element of learning.
The second challenge is to predict the impact of learning one construct on the
ability to answer questions on other constructs. Addressing these challenges
will enable optimisation of students' knowledge acquisition, which can be
deployed in a real edtech solution impacting millions of students. Participants
will run these tasks in an idealised environment with synthetic data and a
real-world scenario with evaluation data collected from a series of A/B tests.
