Causal Machine Learning Frameworks for Robust Predictive Modeling in Dynamic Environments

  • Authors

    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2025PII3S4X

    Published 09-02-2025

  • Causal Machine Learning, Causal Inference, Predictive Modeling, Dynamic Environments, Structural Causal Models, Counterfactual Learning, Domain Adaptation, Distribution Shift, Explainable Artificial Intelligence, Robust Machine Learning

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    M. H. N, “Causal Machine Learning Frameworks for Robust Predictive Modeling in Dynamic Environments”, IJMLPA, vol. 8, no. 2, pp. 01–17, Sep. 2025, doi: 10.67228/3142788X/IJMLPA-2025PII3S4X.

  • Abstract

    Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved predictive analytics across domains such as healthcare, finance, transportation, cybersecurity, manufacturing, and smart cities. However, conventional ML models rely on statistical correlations and often fail under dynamic environments due to concept drift, distribution shifts, and changing causal relationships. Causal Machine Learning (CML) addresses these limitations by integrating causal inference techniques, including structural causal models, directed acyclic graphs (DAGs), counterfactual reasoning, intervention analysis, and invariant causal prediction, to identify true cause-and-effect relationships. This enables more interpretable, robust, and generalizable predictive models. This paper proposes a unified CML framework that combines data preprocessing, causal graph construction, structural causal modeling, causal feature optimization, predictive learning, intervention analysis, and continuous model adaptation. Mathematical formulations support causal dependency estimation, structural equation modeling, invariant risk minimization, and prediction optimization. Experimental results demonstrate improved out-of-distribution prediction, robustness, causal consistency, explainability, and computational efficiency, providing a scalable foundation for trustworthy and adaptive AI in dynamic environments.

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