Causal Machine Learning for Discovering Actionable Insights in Observational Data
DOI:
https://doi.org/10.61453/jods.v20260215Keywords:
Causal Inference, Causal Machine Learning, Observational Data, Decision Intelligence, Explainable AIAbstract
Traditional machine learning models achieve strong predictive performance but often are unable to reliably uncover causal relationships required for reliable decision-making, particularly in observational data where controlled experiments are not feasible. This limitation creates a critical gap between prediction and actionable insight, as correlation-based models are vulnerable to confounding bias and poor generalization under distributional shifts. To address this challenge, this study proposes a unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation. The methodology combines hybrid causal structure learning (constraint-based and score-based approaches) with advanced causal effect estimation methods, including propensity score techniques and doubly robust estimators. The framework is evaluated on both synthetic datasets with known causal structures and real-world datasets to assess its accuracy, robustness, and interpretability. Experiments are conducted using multiple runs with controlled settings to ensure reproducibility and statistical validity. The results demonstrate that the proposed framework significantly outperforms traditional predictive models and standalone causal methods. It achieves higher causal discovery accuracy with improved precision and recall of causal edges, reduces estimation error in Average Treatment Effect (ATE), and maintains stable predictive performance under distributional shifts. Statistical analysis confirms significant improvements (p < 0.01) with large effect sizes, indicating strong reliability and robustness. This research aims to bridge the gap between prediction and explanation by enabling machine learning systems to generate actionable, interpretable, and causally valid insights. The findings highlight the importance of integrating causal reasoning into data science workflows to support informed decision-making, intervention planning, and trustworthy AI development.
References
Arif, S., & MacNeil, M. A. (2023). Applying the structural causal model framework for observational causal inference in ecology. Ecological Monographs, 93(1), e1554. https://doi.org/10.1002/ECM.1554
Austin, P. C., & Fine, J. P. (2025). Inverse Probability of Treatment Weighting Using the Propensity Score with Competing Risks in Survival Analysis. Statistics in Medicine, 44(5), e70009. https://doi.org/10.1002/SIM.70009
Bermann, M., Legarra, A., Munera, A. A., Misztal, I., & Lourenco, D. (2024). Confidence intervals for validation statistics with data truncation in genomic prediction. Genetics Selection Evolution 2024 56:1, 56(1), 18-. https://doi.org/10.1186/S12711-024-00883-W
Chen, L., Ban, T., Wang, X., Lyu, D., & Chen, H. (2025). Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(12), 10990–11002. https://doi.org/10.1109/TPAMI.2025.3594755
D’Agostino McGowan, L., Gerke, T., & Barrett, M. (2024). Causal Inference Is Not Just a Statistics Problem. Journal of Statistics and Data Science Education, 32(2), 150–155. https://doi.org/10.1080/26939169.2023.2276446
Flovik, V. (2024). Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications. https://arxiv.org/pdf/2405.01978
Imbens, G. W. (2024). Causal Inference in the Social Sciences. Annual Review of Statistics and Its Application, 11(1), 123–152. https://doi.org/10.1146/annurev-statistics-033121-114601
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022). Multimodal machine learning in precision health: A scoping review. Npj Digital Medicine 2022 5:1, 5(1), 171-. https://doi.org/10.1038/s41746-022-00712-8
Li, Y., Scheel-Sailer, A., Riener, R., & Paez-Granados, D. (2024). Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals. Scientific Reports 2024 14:1, 14(1), 25067-. https://doi.org/10.1038/s41598-024-75691-9
Liu, Q., Chen, Z., & Wong, W. H. (2024). An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies. Proceedings of the National Academy of Sciences of the United States of America, 121(23), e2322376121. https://doi.org/10.1073/PNAS.2322376121
Luo, H., Zhuang, F., Xie, R., Zhu, H., Wang, D., An, Z., & Xu, Y. (2024). A survey on causal inference for recommendation. Innovation, 5(2), 100590. https://doi.org/10.1016/j.xinn.2024.100590
Maier, R., Grabinger, L., Urlhart, D., & Mottok, J. (2024). Causal Models to Support Scenario-Based Testing of ADAS. IEEE Transactions on Intelligent Transportation Systems, 25(2), 1815–1831. https://doi.org/10.1109/TITS.2023.3317475
Maisonnave, M., Delbianco, F., Tohme, F., Milios, E., & Maguitman, A. G. (2022). Causal graph extraction from news: a comparative study of time-series causality learning techniques. PeerJ Computer Science, 8, e1066. https://doi.org/10.7717/PEERJ-CS.1066
Menzies, T., Hulse, J., Eisty, N. U., Nasir, ·, Eisty, U., & Menzies, · Tim. (2025). Shaky structures: The wobbly world of causal graphs in software analytics. Empirical Software Engineering 2025 30:5, 30(5), 142-. https://doi.org/10.1007/S10664-025-10690-6
Moss, J. (2023). Commentary: Quantile treatment effect of zinc lozenges on common cold duration: A novel approach to analyze the effect of treatment on illness duration. Frontiers in Pharmacology, 14, 1152305. https://doi.org/10.3389/FPHAR.2023.1152305
Nogueira, A. R., Pugnana, A., Ruggieri, S., Pedreschi, D., & Gama, J. (2022). Methods and tools for causal discovery and causal inference. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2), e1449. https://doi.org/10.1002/WIDM.1449
Opitz, J. (2024). A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice. Transactions of the Association for Computational Linguistics, 12, 820–836. https://doi.org/10.1162/tacl_a_00675
Pozzi, M., Noei, S., Robbi, E., Cima, L., Moroni, M., Munari, E., Torresani, E., & Jurman, G. (2024). Generating and evaluating synthetic data in digital pathology through diffusion models. Scientific Reports 2024 14:1, 14(1), 28435-. https://doi.org/10.1038/s41598-024-79602-w
Selamat, S. N., Abd Majid, N., & Mohd Taib, A. (2023). A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia. Sustainability 2023, Vol. 15, 15(1). https://doi.org/10.3390/SU15010861
Semnani, P., & Robeva, E. (2025). Causal structure learning in directed, possibly cyclic, graphical models. Journal of Causal Inference, 13(1). https://doi.org/10.1515/JCI-2024-0037
She, B., Smith, R. L., Pytlarz, I., Sundaram, S., & Paré, P. E. (2024). A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates. PLOS Computational Biology, 20(11), e1012569. https://doi.org/10.1371/JOURNAL.PCBI.1012569
Shi, D., Fairchild, A. J., & Wiedermann, W. (2023). One Step at a Time: A Statistical Approach for Distinguishing Mediators, Confounders, and Colliders Using Direction Dependence Analysis. Psychological Methods. https://doi.org/10.1037/MET0000619
Shi, J., & Norgeot, B. (2022). Learning Causal Effects from Observational Data in Healthcare: A Review and Summary. Frontiers in Medicine, 9, 864882. https://doi.org/10.3389/FMED.2022.864882
Velev, G., & Lessmann, S. (2026). Interpretable, multidimensional evaluation framework for causal discovery from observational i.i.d. data. Information Sciences, 723, 122641. https://doi.org/10.1016/J.INS.2025.122641
Vonk, M. C., Malekovic, N., Bäck, T., & Kononova, A. V. (2023). Disentangling causality: assumptions in causal discovery and inference. Artificial Intelligence Review 2023 56:9, 56(9), 10613–10649. https://doi.org/10.1007/S10462-023-10411-9
Vowels, M. J. (2025). A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists. Psychological Methods. https://doi.org/10.1037/MET0000673
Wang, J. W., Meng, M., Dai, M. W., Liang, P., & Hou, J. (2025). Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy. Frontiers in Immunology, 16, 1630781. https://doi.org/10.3389/FIMMU.2025.1630781
Wang, X., Ban, T., Chen, L., Lyu, D., Zhu, Q., & Chen, H. (2025). Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge. IEEE Transactions on Knowledge and Data Engineering, 37(5), 2695–2711. https://doi.org/10.1109/TKDE.2025.3537832
Wu, H., Shi, W., & Wang, M. D. (2024). Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning. BMC Medical Informatics and Decision Making 2024 24:1, 24(1), 137-. https://doi.org/10.1186/S12911-024-02510-6
Zahoor, S., Liò, P., Dias, G., & Hasanuzzaman, M. (2025). Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data. https://arxiv.org/pdf/2501.15973
Zhang, J., Cammarata, L., Squires, C., Sapsis, T. P., & Uhler, C. (2023). Active learning for optimal intervention design in causal models. Nature Machine Intelligence 2023 5:10, 5(10), 1066–1075. https://doi.org/10.1038/s42256-023-00719-0
Zhao, Y., & Liu, Q. (2023). Causal ML: Python package for causal inference machine learning. SoftwareX, 21, 101294. https://doi.org/10.1016/J.SOFTX.2022.101294
Zhu, Y., Benos, P. V., & Chikina, M. (2024). A hybrid constrained continuous optimization approach for optimal causal discovery from biological data. Bioinformatics, 40(Supplement_2), ii87–ii97. https://doi.org/10.1093/BIOINFORMATICS/BTAE411
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