Causal-Inference Analytics for Detecting Hidden Algorithmic Interventions in Enterprise SaaS Platforms: A Quantitative Framework and Empirical Evaluation
Article Original Website Link: https://www.academicpublishers.org/journals/index.php/ijdsml/article/view/9153Keywords:
Causal inference, algorithmic interventions, SaaS platformsAbstract
Enterprise Software-as-a-Service (SaaS) platforms increasingly rely on complex algorithmic systems that dynamically adjust user experiences, resource allocations, and operational parameters. However, many algorithmic interventions occur without explicit documentation, creating opacity that undermines system reliability, auditability, and trust. This paper develops and validates a quantitative framework for detecting hidden algorithmic interventions using causal inference analytics. We evaluate five causal discovery algorithms, ETIO, Bootstrap-augmented PCMCI+, Differentiable Causal Discovery, Granger Causality, and an Ensemble method, across three intervention scenarios: personalization algorithm changes, resource allocation policy shifts, and microservice configuration modifications. Our empirical results demonstrate that causal inference methods achieve precision rates of 82-94% and recall rates of 78-91% in detecting hidden interventions, significantly outperforming correlation-based baselines. Time-series causal methods excel in temporal scenarios, while ensemble approaches achieve optimal overall performance with F1-scores of 0.89-0.92. This work bridges the gap between causal inference theory and enterprise operational practice, providing deployment-ready guidelines for SaaS operators and establishing reproducible benchmarks for future research.
References
Afriyie, D. (2020). Leveraging predictive people analytics to optimize workforce mobility, talent retention, and regulatory compliance in global enterprises [Working paper]. ResearchGate. https://www.researchgate.net/profile/Derrick-Afriyie/publication/3944356482.Almulla, S., Iraqi, Y., & Wolthusen, S. D. (2015). Inferring relevance and presence of evidence in service-oriented and SaaS architectures. In 2015 IEEE International Symposium on Computers and Communications (ISCC) (pp. 506-511). IEEE. https://doi.org/10.1109/ISCC.2015.74055093.Borboudakis, G., & Tsamardinos, I. (2016). Towards robust and versatile causal discovery for business applications. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1435-1444). ACM. https://doi.org/10.1145/2939672.29398724.Debeire, K., Runge, J., Gerhardus, A., & Eyring, V. (2021). Bootstrap aggregation and confidence measures to improve time series causal discovery. arXiv preprint arXiv:2306.08946v2. https://arxiv.org/abs/2306.08946v2
AMERICAN ACADEMIC PUBLISHERhttps://www.academicpublishers.org/journals/index.php/ijdsml415.Duong, T. D., Li, Q., & Xu, G. (2021). Stochastic intervention for causal effect estimation. arXiv preprint arXiv:Artificial Intelligence. https://scispace.com/papers/stochastic-intervention-for-causal-effect-estimation-4o4dtdiefp6.Faria, G. R. A., Martins, A. F. T., & Figueiredo, M. A. T. (2021). Differentiable causal discovery under latent interventions. arXiv preprint arXiv:2203.02336v1. https://arxiv.org/abs/2203.02336v17.Kiciman, E., & Thelin, J. (2018). Answering what if, should I, and other expectation exploration queries using causal inference over longitudinal data. In Proceedings of the Conference (pp. 1-10). https://scispace.com/papers/answering-what-if-should-i-and-other-expectation-exploration-1t6ei70p238.Lin, A. Y., Merchant, A., Sarkar, S. K., & D'Amour, A. (2019). Universal causal evaluation engine: An API for empirically evaluating causal inference models. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1-9). ACM. https://scispace.com/papers/universal-causal-evaluation-engine-an-api-for-empirically-4iee57lzkd9.Meng, Y., Zhang, S., Sun, Y., Zhang, R., & Hu, Z. (2020). Localizing failure root causes in a microservice through causality inference. In 2020 IEEE/ACM International Workshop on Quality of Service (IWQoS) (pp. 1-10). IEEE. https://doi.org/10.1109/IWQOS49365.2020.921305810.Thalheim, J., Rodrigues, A. W. D. O., Akkus, I. E., Bhatotia, P., & Chen, R. (2017). Sieve: Actionable insights from monitored metrics in distributed systems. In Proceedings of the ACM Conference (pp. 1-14). ACM. https://doi.org/10.1145/3135974.313597711.Wang, P., Xu, J., Ma, M., Lin, W., & Pan, D. (2018). CloudRanger: Root cause identification for cloud native systems. In 2018 IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (pp. 492-502). IEEE. https://doi.org/10.1109/CCGRID.2018.0007612.Wong, J. (2020). Computational causal inference. arXiv preprint arXiv: Computation. https://scispace.com/papers/computational-causal-inference-11aj8o7gfl13.Xu, Y., Zhang, X., Luo, C., Qin, S.,& Pandey, R. (2021). CARE: Infusing causal aware thinking to root cause analysis in cloud system. In Proceedings of the ACM Conference (pp. 1-10). ACM. https://doi.org/10.1145/3447851.345873714.Yoganarasimhan, H., & Barzegary, E. (2019). Design and evaluation of personalized targeting policies: Application to free trials [Working paper]. University of Washington. http://faculty.washington.edu/hemay/Design_Evaluation_November_2019.pdf15.Yoganarasimhan, H., Barzegary, E., & Pani, A. (2020). Design and evaluation of personalized free trials. arXiv preprint arXiv:Machine Learning. https://scispace.com/papers/design-and-evaluation-of-personalized-free-trials-5ephy2ggoc16.Zhang, Y., Guan, Z., Qian, H., Xu, L., & Liu, H. (2021). CloudRCA: A root cause analysis framework forcloud computing platforms. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (pp. 4373-4382). ACM. https://doi.org/10.1145/3459637.3481903
