Causal Machine Learning for Fair Allocation of Scarce Public Resources

Marcus E. Holloway

Causal Machine Learning for Fair Allocation of Scarce Public Resources

Author(s)	Dr. Marcus E. Holloway
Country	United States
Abstract	Public institutions routinely allocate resources whose demand exceeds available supply, including subsidized housing, healthcare interventions, social assistance, educational support, emergency relief, and employment services. Conventional allocation systems often prioritize individuals according to observed need, predicted future risk, waiting time, categorical eligibility, or administrative discretion. Causal machine learning introduces a different analytical possibility by estimating how strongly different individuals or groups are expected to benefit from a particular intervention rather than merely predicting who is currently most disadvantaged or most likely to experience an adverse outcome. Research on heterogeneous treatment effects, causal forests, generalized random forests, and policy learning demonstrates how flexible statistical methods can support treatment targeting under resource constraints. Athey and Wager show that policy learning can optimize treatment assignment from observational data under appropriate causal identification assumptions and application-specific constraints, while Kim and Zubizarreta demonstrate that heterogeneous treatment-effect estimation can itself be conducted under fairness constraints. This paper develops a governance-oriented framework for applying causal machine learning to scarce public-resource allocation. The proposed approach integrates causal identification, heterogeneous treatment-effect estimation, constrained policy learning, distributional fairness, uncertainty assessment, human oversight, transparency, and contestability. A simulation-based analysis is used to demonstrate the potential tension between outcome efficiency and equity without presenting synthetic values as empirical government results. The simulated fairness–efficiency frontier shows that moderate and high fairness constraints may substantially improve equity while preserving a large proportion of estimated aggregate benefit, whereas extremely restrictive constraints can produce a larger efficiency cost. The study argues that causal targeting should never be interpreted as a purely technical ranking exercise. Allocation legitimacy depends on normative choices concerning whose outcomes count, which inequalities require correction, whether protected attributes may be used to improve substantive fairness, how uncertainty is handled, and which individuals retain meaningful opportunities to challenge automated recommendations. Causal machine learning can contribute to fairer allocation only when causal validity and distributive justice are jointly designed rather than treated as independent objectives.
Keywords	causal machine learning, scarce public resources, heterogeneous treatment effects, policy learning, algorithmic fairness, causal forests, public administration, resource allocation, distributive justice, responsible artificial intelligence
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2022
Published On	2022-01-03

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American Journal of Advanced Multidisciplinary Innovation and Research

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Causal Machine Learning for Fair Allocation of Scarce Public Resources

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