Causal Inference in HR Analytics with Directed Acyclic Graphs

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Guenole, Nigel and Charlwood, Andy. 2025. Causal Inference in HR Analytics with Directed Acyclic Graphs. In: Martin R. Edwards; Dana Minbaeva; Alec Levenson and Mark A. Huselid, eds. Workforce Analytics: A Global Perspective. Abingdon: Routledge, pp. 77-84. ISBN 9781032039848 [Book Section]

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Abstract or Description

People analytics is a nascent field that has yet to embrace the topic of causal inference. Yet understanding causality is often critically important in human resources. In some areas of management, accurate training predictions from a machine learning model might be enough to justify a decision, such as about a website design to maximize sales. As long as sales increase, it doesn’t really matter what causes customers to buy more on one website compared to the next. In contrast, with people analytics problems, causes do matter. Practitioners often look to change behavior with interventions, which requires an understanding of causality. Consider the results of an engagement survey, where engagement is found to be correlated with employee turnover. Is low engagement the cause of higher turnover, or is it merely associated with it, with other unobserved variables—like poor line management—perhaps causing both low engagement and high turnover? The answer determines the right action to reduce turnover.

Item Type:

Book Section

Identification Number (DOI):

https://doi.org/10.4324/9781003190097

Additional Information:

"This is an Accepted Manuscript of a book chapter published by Routledge/ in Workforce Analytics: A Global Perspective on 2 April 2025, available online: https://www.routledge.com/Workforce-Analytics-A-Global-Perspective/Edwards-Minbaeva-Levenson-Huselid/p/book/9781032029009. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.”