Causal Inference Models in Strategic Marketing Investment Decisions

  • Authors

    • Dr. Deepak Mishra Professor, University of Lucknow, India. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PII1K4X

    Published 07-04-2022

  • Causal Inference, Strategic Marketing, Marketing Roi, Marketing Analytics, Causal Machine Learning, Propensity Score Matching, Difference-In-Differences, Instrumental Variables, Uplift Modeling, Decision Science

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    D. Mishra, “Causal Inference Models in Strategic Marketing Investment Decisions”, IJMLPA, vol. 5, no. 2, pp. 01–13, Jul. 2022, doi: 10.67228/3142788X/IJMLPA-2022PII1K4X.

  • Abstract

    Strategic marketing investment decisions are often guided by correlation-based metrics, which may misrepresent the true effectiveness of marketing initiatives. Causal inference models provide a robust framework to uncover the actual cause-and-effect relationships between marketing actions and business outcomes such as sales, customer retention, and brand equity. This paper explores the application of causal inference techniques—including propensity score matching, difference-in-differences, instrumental variables, and causal machine learning—in the context of strategic marketing. Through a detailed review of empirical studies and simulated experiments, we demonstrate how causal models can help organizations allocate marketing budgets more effectively, optimize channel strategies, and measure true return on investment (ROI). We also address key challenges such as data limitations, confounding bias, and model interpretability. The paper concludes by proposing a practical roadmap for integrating causal analytics into modern marketing decision-making frameworks.

  • References

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    [6] Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

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    [9] Bonhomme, S., Lamadon, T., & Manresa, E. (2021). Discretizing unobserved heterogeneity. Econometrica, 89(1), 229–261. (applied in treatment heterogeneity analysis)

    [10] Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217–240.

    [11] Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.

    [12] Blundell, R., & Costa Dias, M. (2009). Alternative approaches to evaluation in empirical microeconomics. Journal of Human Resources, 44(3), 565–640.

    [13] Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. (context for AI integration)

    [14] Hansotia, B., & Rukstales, B. (2002). Incremental value modeling. Journal of Interactive Marketing, 16(3), 35–46. (early applications of uplift modeling in marketing)

    [15] Berman, R., & Kim, J. (2021). The causal impact of marketing: A structured literature review. Marketing Science, 40(2), 197–220.

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