ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
Discovering Causal Factors Influencing Student Academic Performance Using Causal Discovery and Inference
Biswaraj Baral1
Taposh Dutta Roy2
Bhesh Bahadur Thapa3
1 Data Science Group, On Treat Services, Kathmandu, Nepal. 2 Data Science Research, Silicon Valley Quantum Computing Group, San Ramon, CA, USA. 3 School of Engineering, Pokhara University, Nepal.
Published Online: September-December 2026
Pages: 188-195
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503024
References
1. Pearl J. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press. 2009;2nd ed.
2. Luo Z, Mai J, Feng C, Kong D, Liu J, Ding Y, Qi B, Zhu Z. A method for prediction and analysis of student performance that combines multi-dimensional features of time and space. Mathematics. 2024;12(22):3597.
3. Boujmiraz S, Darhmaoui H, Drissi el maliani A. Predicting student performance: A comprehensive review of machine learning, deep learning, and explainable AI approaches. Computers and Education: Artificial Intelligence. 2026;10:100548.
4. Fazil M, Rísquez A, Halpin C. A novel deep learning model for student performance prediction using engagement data. Journal of Learning Analytics. 2024;11(2):23–41.
5. Kalisch M, Bühlmann P. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research. 2007;8(22):613–636.
6. Chickering DM. Optimal structure identification with greedy search. Journal of Machine Learning Research. 2002;3:507–554.
7. Shimizu S, Hoyer PO, Hyvärinen A, Kerminen A. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research. 2006;7:2003–2030.
8. Shimizu S, Inazumi T, Sogawa Y, Hyvärinen A, Kawahara Y, Washio T, Hoyer PO, Bollen K. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research. 2011;12:1225–1248.
9. Zheng X, Aragam B, Ravikumar P, Xing EP. DAGs with NO TEARS: continuous optimization for structure learning. Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018:9492–9503.
10. Ng I, Ghassami A, Zhang K. On the role of sparsity and DAG constraints for learning linear DAGs. Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020.
11. Ayem GT, Asilkan O, Iorliam A, Ibrahim R, Thandekkattu SG. Causal inference estimates with backdoor adjustment condition vs. the unconfoundedness assumption: A comparative analysis study of the structural causal model and the potential outcome frameworks. ESP Journal of Engineering & Technology Advancements. 2023;3(4):1–22.
12. Andrew BY, Brookhart MA, Pearse R, Raghunathan K, Krishnamoorthy V. Propensity score methods in observational research: brief review and guide for authors. British Journal of Anaesthesia. 2023;131(5):805–809.
13. Wu A, Kuang K, Xiong R, Wu F. Instrumental variables in causal inference and machine learning: A survey. ACM Computing Surveys. 2025;57(11):292.
14. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669–688.
15. Gani MO, Kethireddy S, Adib R, Hasan U, Griffin P, Adibuzzaman M. Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU. Artificial Intelligence in Medicine. 2023;137:102493.
16. Cortez P. Student Performance. UCI Machine Learning Repository. 2008. DOI: https://doi.org/10.24432/C5TG7T.
17. Li A, Jaber A, Bareinboim E. Causal discovery from observational and interventional data across multiple environments. CausalAI Laboratory, Columbia University. 2023;R-98.
18. Israel D, Grover A, Van den Broeck G. High dimensional causal inference with variational backdoor adjustment. 2023. arXiv:2310.06100.
19. Xu Z, Cheng D, Li J, Liu J, Liu L, Yu K. Causal inference with conditional front-door adjustment and identifiable variational autoencoder. 2023. arXiv:2310.01937.
20. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Statistics in Medicine. 2014;33(13):2297–2340.
21. Ho F. Regression adjustment for causal inference. BMJ Medicine. 2025;4(1).
2. Luo Z, Mai J, Feng C, Kong D, Liu J, Ding Y, Qi B, Zhu Z. A method for prediction and analysis of student performance that combines multi-dimensional features of time and space. Mathematics. 2024;12(22):3597.
3. Boujmiraz S, Darhmaoui H, Drissi el maliani A. Predicting student performance: A comprehensive review of machine learning, deep learning, and explainable AI approaches. Computers and Education: Artificial Intelligence. 2026;10:100548.
4. Fazil M, Rísquez A, Halpin C. A novel deep learning model for student performance prediction using engagement data. Journal of Learning Analytics. 2024;11(2):23–41.
5. Kalisch M, Bühlmann P. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research. 2007;8(22):613–636.
6. Chickering DM. Optimal structure identification with greedy search. Journal of Machine Learning Research. 2002;3:507–554.
7. Shimizu S, Hoyer PO, Hyvärinen A, Kerminen A. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research. 2006;7:2003–2030.
8. Shimizu S, Inazumi T, Sogawa Y, Hyvärinen A, Kawahara Y, Washio T, Hoyer PO, Bollen K. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research. 2011;12:1225–1248.
9. Zheng X, Aragam B, Ravikumar P, Xing EP. DAGs with NO TEARS: continuous optimization for structure learning. Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018:9492–9503.
10. Ng I, Ghassami A, Zhang K. On the role of sparsity and DAG constraints for learning linear DAGs. Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020.
11. Ayem GT, Asilkan O, Iorliam A, Ibrahim R, Thandekkattu SG. Causal inference estimates with backdoor adjustment condition vs. the unconfoundedness assumption: A comparative analysis study of the structural causal model and the potential outcome frameworks. ESP Journal of Engineering & Technology Advancements. 2023;3(4):1–22.
12. Andrew BY, Brookhart MA, Pearse R, Raghunathan K, Krishnamoorthy V. Propensity score methods in observational research: brief review and guide for authors. British Journal of Anaesthesia. 2023;131(5):805–809.
13. Wu A, Kuang K, Xiong R, Wu F. Instrumental variables in causal inference and machine learning: A survey. ACM Computing Surveys. 2025;57(11):292.
14. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669–688.
15. Gani MO, Kethireddy S, Adib R, Hasan U, Griffin P, Adibuzzaman M. Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU. Artificial Intelligence in Medicine. 2023;137:102493.
16. Cortez P. Student Performance. UCI Machine Learning Repository. 2008. DOI: https://doi.org/10.24432/C5TG7T.
17. Li A, Jaber A, Bareinboim E. Causal discovery from observational and interventional data across multiple environments. CausalAI Laboratory, Columbia University. 2023;R-98.
18. Israel D, Grover A, Van den Broeck G. High dimensional causal inference with variational backdoor adjustment. 2023. arXiv:2310.06100.
19. Xu Z, Cheng D, Li J, Liu J, Liu L, Yu K. Causal inference with conditional front-door adjustment and identifiable variational autoencoder. 2023. arXiv:2310.01937.
20. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Statistics in Medicine. 2014;33(13):2297–2340.
21. Ho F. Regression adjustment for causal inference. BMJ Medicine. 2025;4(1).
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