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The Case for Causal AI

Much of artificial intelligence (AI) in common use is dedicated to predicting people’s behavior. It tries to anticipate your next purchase, your next mouse-click, your next job move. But such techniques can run into problems when they are used to analyze data for health and development programs. If we do not know the root causes of behavior, we could easily make poor decisions and support ineffective and prejudicial policies.

AI, for example, has made it possible for health-care systems to predict which patients are likely to have the most complex medical needs. In the United States, risk-prediction software is being applied to roughly 200 million people to anticipate which patients would benefit from extra medical care now, based on how much they are likely to cost the health-care system in the future. It employs predictive machine learning, a class of self-adaptive algorithms that improve their accuracy as they are provided new data. But as health researcher Ziad Obermeyer and his colleagues showed in a recent article in Science magazine, this particular tool had an unintended consequence: black patients who had more chronic illnesses than white patients were not flagged as needing extra care.

What went wrong? The algorithm used insurance claims data to predict patients’ future health needs based on their recent health costs. But the algorithm’s designers had not taken into account that health-care spending on black Americans is typically lower than on white Americans with similar health conditions, for reasons unrelated to how sick they are—such as barriers to health-care access, inadequate health care, or lack of insurance. Using health-care costs as a proxy for illness led the predictive algorithm to make recommendations that were accurate for white patients—lower health-care spending was the consequence of fewer health conditions—but perpetuated racial biases in care for black patients. The researchers notified the manufacturer, which ran tests using its own data, confirmed the problem, and collaborated with the researchers to remove the bias from the algorithm.

This story illustrates one of the perils of certain types of AI. No matter how sophisticated, predictive algorithms and their users can fall into the trap of equating correlation with causation—in other words, of thinking that because event X precedes event Y, X must be the cause of Y. A predictive model is useful for establishing the correlation between an event and an outcome. It says, “When we observe X, we can predict that Y will occur.” But this is not the same as showing that Y occurs because of X. In the case of the health-care algorithm, higher rates of illness (X) were correctly correlated with higher health-care costs (Y) for white patients. X caused Y, and it was therefore accurate to use health-care costs as a predictor of future illness and health-care needs. But for black patients, higher rates of illness did not in general lead to higher costs, and the algorithm would not accurately predict their future health-care needs. There was correlation but not causation.

This matters as the world increasingly turns to AI to help solve pressing health and development challenges. Relying solely on predictive models of AI in areas as diverse as health care, justice, and agriculture risks devastating consequences when correlations are mistaken for causation. Therefore, it is imperative that decision makers also consider another AI approach—causal AI, which can help identify the precise relationships of cause and effect. Identifying the root causes of outcomes is not causal AI’s only advantage; it also makes it possible to model interventions that can change those outcomes, by using causal AI algorithms to ask what-if questions. For example, if a specific training program is implemented to improve teacher competency, by how much should we expect student math test scores to improve? Simulating scenarios to evaluate and compare the potential effect of an intervention (or group of interventions) on an outcome avoids the time and expense of lengthy tests in the field.

The original article can be found here.

It is useful to note that AI and causality has been contributed by professor Judea Pearl, who is awarded Turing Award 2011. In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea Pearl also contribute on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.