Causal Inference in HR Analytics with Directed Acyclic Graphs

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.