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<p>This paper argues that causal inference is a necessary condition for achieving fairness in algorithmic decision-making. Dominant machine learning models are typically limited to associative methods. However, we often need to modify the very probability distributions that produce social injustice, not merely identify predictive patterns from them, an undertaking standard machine learning neglects. Fairness often requires identifying who or what is responsible for a particular outcome of interest, uncovering the source of disparities, and determining which policies are effective interventions to address these issues. Normative questions such as these require causal modeling. I then confront a key objection: certain social variables, notably race, appear to violate modularity in causal models. To address this, I propose several strategies, including bracketing subsystems into coarser causal abstractions/macro-variables, refining the study design, and relaxing local invariances to handle these non-local influences. In sum, I claim that causal modeling is indispensable for responsible algorithmic decision-making.</p>

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Why causal inference is necessary for algorithmic fairness

  • 期刊论文

  • 发表日期：

    2025年9月10日

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  • Alexander Williams Tolbert

摘要

This paper argues that causal inference is a necessary condition for achieving fairness in algorithmic decision-making. Dominant machine learning models are typically limited to associative methods. However, we often need to modify the very probability distributions that produce social injustice, not merely identify predictive patterns from them, an undertaking standard machine learning neglects. Fairness often requires identifying who or what is responsible for a particular outcome of interest, uncovering the source of disparities, and determining which policies are effective interventions to address these issues. Normative questions such as these require causal modeling. I then confront a key objection: certain social variables, notably race, appear to violate modularity in causal models. To address this, I propose several strategies, including bracketing subsystems into coarser causal abstractions/macro-variables, refining the study design, and relaxing local invariances to handle these non-local influences. In sum, I claim that causal modeling is indispensable for responsible algorithmic decision-making.