Causal inference in higher education: Building better curriculums

Prableen Kaur, Agoritsa Polyzou, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Higher educational institutions constantly look for ways to meet students’ needs and support them through graduation. Recent work in the field of learning analytics have developed methods for grade prediction and course recommendations. Although these methods work well, they often fail to discover causal relationships between courses, which may not be evident through correlation-based methods. In this work, we aim at understanding the causal relationships between courses to aid universities in designing better academic pathways for students and to help them make better choices. Our methodology employs methods of causal inference to study these relationships using historical student performance data. We make use of a doubly-robust method of matching and regression in order to obtain the casual relationship between a pair of courses. The results were validated by the existing prerequisite structure and by cross-validation of the regression model. Further, our approach was also tested for robustness and sensitivity to certain hyper parameters. This methodology shows promising results and is a step forward towards building better academic pathways for students.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th 2019 ACM Conference on Learning at Scale, L@S 2019
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450368049
StatePublished - Jun 24 2019
Event6th ACM Conference on Learning at Scale, L@S 2019 - Chicago, United States
Duration: Jun 24 2019Jun 25 2019

Publication series

NameProceedings of the 6th 2019 ACM Conference on Learning at Scale, L@S 2019


Conference6th ACM Conference on Learning at Scale, L@S 2019
Country/TerritoryUnited States


  • Average Treatment Effect
  • Causal Inference
  • Learning Analytics
  • Matching


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