DOI : https://doi.org/10.62527/ijasce.5.3.177

Design and Validation of Structural Causal Model: A focus on SENSE-EGRA Datasets

Gabriel Terna Ayem (1), Augustine Shey Nsang (2), Bernard Igoche Igoche (3), Garba Naankang (4)
(1) American University of Nigeria, Yola
(2) American University of Nigeria, Yola
(3) School of Computing, University of Portsmouth
(4) School of Aerospace, Transport, and Manufacturing, Cranfield University
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How to cite (IJASEIT) :
Ayem, G. T., Nsang, A. S., Igoche, B. I., & Naankang, G. (2023). Design and Validation of Structural Causal Model: A focus on SENSE-EGRA Datasets. International Journal of Advanced Science Computing and Engineering, 5(3), 257–268. https://doi.org/10.62527/ijasce.5.3.177
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Designing and validation of causal model correctness from a dataset whose background knowledge is gotten from a research process is not a common phenomenon. In fact, studies have shown that in many critical areas such as healthcare and education, researchers develop models from direct acyclic graphs without testing them. This phenomenon is worrisome and is bound to cast a dark shadow on the inference estimates that many arise from such models. In this study, we have design a novel application-based SCM for the first time using the background knowledge gotten from the American university of Nigeria (AUN), Yola, on the letter identification subtask of Early Grade reading Assessment (EGRA) program on Strengthen Education in Northeast Nigeria (SENSE-EGRA) project dataset, which was sponsored by the USAID. We employed the conditional independence test (CIT) criteria for the model’s correctness validation testing, and the results shows a near perfect SCM.

Box, J.F., RA Fisher, the Life of a Scientist. Revue Philosophique de la France Et de l, 1980. 170(4).

Benson, K. and A.J. Hartz, A comparison of observational studies and randomized, controlled trials. New England Journal of Medicine, 2000. 342(25): p. 1878-1886.

Silverman, S.L., From randomized controlled trials to observational studies. The American journal of medicine, 2009. 122(2): p. 114-120.

Tennant, P.W., et al., Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International journal of epidemiology, 2021. 50(2): p. 620-632.

Guo, R., et al., A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR), 2020. 53(4): p. 1-37.

Hitchcock, C. and M. Rédei, Reichenbach’s common cause principle. 2020.

Pearl, J., Causality. 2009: Cambridge university press.

Peters, J., D. Janzing, and B. Schölkopf, Elements of causal inference: foundations and learning algorithms. 2017: The MIT Press.

Neyman, J., Sur les applications de la theorie des probabilites aux experiences agricoles: essai des principes (Masters Thesis); Justification of applications of the calculus of probabilities to the solutions of certain questions in agricultural experimentation. Excerpts English translation (Reprinted). Stat Sci, 1923. 5: p. 463-472.

Rubin, D.B., Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 1974. 66(5): p. 688.

Spirtes, P., C. Glymour, and R. Scheines, Discovery algorithms for causally sufficient structures, in Causation, prediction, and search. 1993, Springer. p. 103-162.

Greenland, S., J. Pearl, and J.M. Robins, Causal diagrams for epidemiologic research. Epidemiology, 1999: p. 37-48.

Lauritzen, S.L., Causal Inference from. Complex stochastic systems, 2000: p. 63.

Eberhardt, F., Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics, 2017. 3(2): p. 81-91.

Halpern, J.Y., The Book of Why, Judea Pearl, Basic Books (2018). 2019, Elsevier.

Neal, B., Introduction to causal inference from a machine learning perspective. Course Lecture Notes (draft), 2020.

Elwert, F., Graphical causal models, in Handbook of causal analysis for social research. 2013, Springer. p. 245-273.

Nogueira, A.R., et al., Methods and tools for causal discovery and causal inference. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2022: p. e1449.

Yao, L., et al., A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021. 15(5): p. 1-46.

Glymour, C., K. Zhang, and P. Spirtes, Review of causal discovery methods based on graphical models. Frontiers in genetics, 2019. 10: p. 524.

Pearl, J., Probabilistic reasoning in intelligent systems: networks of plausible inference. 1988: Morgan kaufmann.

Gultchin, L., et al. Differentiable causal backdoor discovery. at the International Conference on Artificial Intelligence and Statistics. 2020. PMLR.

Correa, J. and E. Bareinboim. Causal effect identification by adjustment under confounding and selection biases. in Proceedings of the AAAI Conference on Artificial Intelligence. 2017.

Tian, J. and J. Pearl, A general identification condition for causal effects. 2002: eScholarship, University of California.

Pearl, J. and D. Mackenzie, The book of why: the new science of cause and effect. 2018: Basic books.

Pearl, J., Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016, 2018.

Richardson, T.S. and J.M. Robins. Single world intervention graphs: a primer. in Second UAI workshop on causal structure learning, Bellevue, Washington. 2013. Citeseer.

Zhang, J. and P. Spirtes, Intervention, determinism, and the causal minimality condition. Synthese, 2011. 182(3): p. 335-347.

Hauser, A. and P. Bühlmann, Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. The Journal of Machine Learning Research, 2012. 13(1): p. 2409-2464.

Hauser, A. and P. Bühlmann, Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2015. 77(1): p. 291-318.

Mayrhofer, R. and M.R. Waldmann, Sufficiency and necessity assumptions in causal structure induction. Cognitive science, 2016. 40(8): p. 2137-2150.

Zhang, J. and W. Mayer, Weakening faithfulness: some heuristic causal discovery algorithms. International journal of data science and analytics, 2017. 3(2): p. 93-104.

Zhang, J. and P.L. Spirtes, Strong faithfulness and uniform consistency in causal inference. arXiv preprint arXiv:1212.2506, 2012.

Shpitser, I., T. VanderWeele, and J.M. Robins, On the validity of covariate adjustment for estimating causal effects. arXiv preprint arXiv:1203.3515, 2012.

Ankan, A., I.M. Wortel, and J. Textor, Testing graphical causal models using the R package “dagitty”. Current Protocols, 2021. 1(2): p. e45.

Thoemmes, F., Y. Rosseel, and J. Textor, Local fit evaluation of structural equation models using graphical criteria. Psychological methods, 2018. 23(1): p. 27.

Pearl, J. and T.S. Verma, A theory of inferred causation, in Studies in Logic and the Foundations of Mathematics. 1995, Elsevier. p. 789-811.

Pearl, J. and E. Bareinboim. Transportability of causal and statistical relations: A formal approach. in Twenty-fifth AAAI conference on artificial intelligence. 2011.

Pearl, J., Causal inference in statistics: An overview. Statistics surveys, 2009. 3: p. 96-146.