Causal Discovery and Counterfactual Explanations for Personalized
  Student Learning
Bevan I. Smith
The paper focuses on identifying the causes of student performance to provide
personalized recommendations for improving pass rates. We introduce the need to
move beyond predictive models and instead identify causal relationships. We
propose using causal discovery techniques to achieve this. The study's main
contributions include using causal discovery to identify causal predictors of
student performance and applying counterfactual analysis to provide
personalized recommendations. The paper describes the application of causal
discovery methods, specifically the PC algorithm, to real-life student
performance data. It addresses challenges such as sample size limitations and
emphasizes the role of domain knowledge in causal discovery. The results reveal
the identified causal relationships, such as the influence of earlier test
grades and mathematical ability on final student performance. Limitations of
this study include the reliance on domain expertise for accurate causal
discovery, and the necessity of larger sample sizes for reliable results. The
potential for incorrect causal structure estimations is acknowledged. A major
challenge remains, which is the real-time implementation and validation of
counterfactual recommendations. In conclusion, the paper demonstrates the value
of causal discovery for understanding student performance and providing
personalized recommendations. It highlights the challenges, benefits, and
limitations of using causal inference in an educational context, setting the
stage for future studies to further explore and refine these methods.