Causal Machine Learning for Identifying Hidden Drivers of Customer Behavior in Digital Platforms
Keywords:
causal machine learning, customer behavior, digital platforms, hidden drivers, counterfactual inference, structural causal models, heterogeneous treatment effects, algorithmic fairness, platform governanceAbstract
Digital platforms generate vast quantities of observational data on customer interactions, yet the underlying causal mechanisms that drive behavior remain opaque. Traditional predictive machine learning models capture correlations but fail to distinguish true drivers from spurious associations, limiting their utility for intervention design and policy formulation. This paper presents a comprehensive framework for applying causal machine learning to identify hidden drivers of customer behavior within large-scale digital ecosystems. We begin by reviewing the theoretical foundations of causal inference, including the potential outcomes framework, structural causal models, and double machine learning, emphasizing their applicability to high-dimensional observational data. A methodological architecture is proposed that integrates causal discovery, heterogeneous treatment effect estimation, and robust validation strategies tailored to platform settings where treatments are non-random, confounders are numerous, and feedback loops are prevalent. The discussion examines critical structural trade-offs involving model interpretability, computational scalability, fairness across user groups, and the sustainability of deployed causal systems under shifting platform dynamics. Governance and policy implications are explored, particularly regarding algorithmic transparency, accountability for automated decisions, and the regulatory challenges posed by proprietary data. A case illustration synthesizes a realistic deployment scenario to highlight practical considerations. The paper concludes by outlining open research directions, including dynamic causal modeling, integration with reinforcement learning, and the development of benchmark environments for causal machine learning in digital platforms. Our analysis underscores the necessity of shifting from correlation-based analytics to causal reasoning for robust, equitable, and actionable insight generation.
References
1. J. Pearl, Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press, 2009.
2. S. L. Morgan and C. Winship, Counterfactuals and Causal Inference: Methods and Principles for Social Research, 2nd ed. Cambridge University Press, 2015.
3. R. Athey and G. W. Imbens, “Machine learning methods that economists should know about,” Annual Review of Economics, vol. 11, pp. 685–725, 2019.
4. S. Athey and G. W. Imbens, “The state of applied econometrics: Causality and policy evaluation,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 3–32, 2017.
5. M. A. Hernán and J. M. Robins, Causal Inference: What If. Chapman & Hall/CRC, 2020.
6. D. B. Rubin, “Estimating causal effects of treatments in randomized and nonrandomized studies,” Journal of Educational Psychology, vol. 66, no. 5, pp. 688–701, 1974.
7. P. R. Rosenbaum and D. B. Rubin, “The central role of the propensity score in observational studies for causal effects,” Biometrika, vol. 70, no. 1, pp. 41–55, 1983.
8. J. Pearl, “Causal inference in statistics: An overview,” Statistics Surveys, vol. 3, pp. 96–146, 2009.
9. J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, 2017.
10. V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins, “Double/debiased machine learning for treatment and structural parameters,” The Econometrics Journal, vol. 21, no. 1, pp. C1–C68, 2018.
11. S. Athey and G. W. Imbens, “Recursive partitioning for heterogeneous causal effects,” Proceedings of the National Academy of Sciences, vol. 113, no. 27, pp. 7353–7360, 2016.
12. S. Wager and S. Athey, “Estimation and inference of heterogeneous treatment effects using random forests,” Journal of the American Statistical Association, vol. 113, no. 523, pp. 1228–1242, 2018.
13. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, 2nd ed. MIT Press, 2000.
14. R. Silva and R. Scheines, “Causal inference for latent variable models with application to gene regulatory networks,” Journal of Machine Learning Research, vol. 10, pp. 123–156, 2009.
15. O. M. Dekel, P. M. Domeniconi, and M. C. Mozer, “Bayesian structure learning with a complete graph prior: Theory and applications,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
16. P. R. Rosenbaum, Design of Observational Studies. Springer, 2010.
17. S. Bubeck and N. Cesa-Bianchi, “Regret analysis of stochastic and nonstochastic multi-armed bandit problems,” Foundations and Trends in Machine Learning, vol. 5, no. 1, pp. 1–122, 2012.
18. S. S. Hernán, B. Brumback, and J. M. Robins, “Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men,” Epidemiology, vol. 11, no. 5, pp. 561–570, 2000.
19. S. Barocas and A. D. Selbst, “Big data’s disparate impact,” California Law Review, vol. 104, no. 3, pp. 671–732, 2016.
20. M. J. Kusner, J. R. Loftus, C. Russell, and R. Silva, “Counterfactual fairness,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
21. J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Computing Surveys, vol. 46, no. 4, pp. 1–37, 2014.
22. R. K. Varshney, “Trustworthy machine learning and artificial intelligence,” in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019, pp. 1–2.
23. European Commission, “Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act),” COM(2021) 206 final, 2021.
24. C. Dwork, A. Roth, and D. Wagner, “Concentrated differential privacy,” in Proceedings of the 2014 ACM Conference on Computer and Communications Security, 2014, pp. 295–306.
25. L. Floridi and M. Taddeo, “What is data ethics?” Philosophical Transactions of the Royal Society A, vol. 374, no. 2083, 20160060, 2016.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
