While generative artificial intelligence (AI) has been grabbing most of the attention thanks to the likes of ChatGPT and Bard, another use of AI that has not generated the same level of buzz—but likely will—may also prove useful to pharma marketers—causal AI.
Sometimes referred to as “counterfactual analysis” and “artificial imagination,” causal AI identifies the underlying mechanisms of a cause and effect, therefore providing a clearer picture of the “why” behind challenging scenarios. So, in pharma for example, while generative AI may be able to list possible treatments for a disease, causal AI can be used to identify cause-and-effect relationships of various biomarkers on disease. The result is a better understanding of the causal relationship between a treatment and specific patients.
For pharma marketers, this use of machine learning offers three distinct benefits to boost the accuracy—and efficacy—of campaigns:
1. Deeper Insights
With the ability to identify the causal relationships between variables, marketers will have a clearer picture of which channels deliver the most effective message, using algorithms to create causal relationships between demographics, social media habits, website usage, and engagement. Recently, causal AI has helped large companies—such as Amazon and Google—analyze pertinent information regarding demographics, and usage patterns, to more accurately predict buying behaviors. For pharma marketing, imagine the power of truly understanding drivers of non-adherence for specific patient populations to drive a successful campaign—a campaign that leads to better patient outcomes.
2. Less Bias
Causal AI can actually detect holes in a dataset to identify and correct inequities and assumptions. The concept of racial bias, for example, can be masked by socioeconomic status, or even poor access to healthcare. A recent study in Circulation, the Journal of the American Heart Association, reports researchers using causal AI and propensity score matching to discover a substantial number of Black patients were less likely to receive care for heart failure than white patients.1
3. Better Decision-Making
CausaLens, builder of causal AI-powered products, notes that causal AI is the way forward as, “the only technology that can augment human decision-making in marketing.” When it comes to performance and the ability to explain complex models that are full of variables, causal AI, according to DeepMind, “…could enable a deeper understanding of complex systems and allow us to better align decision systems with society’s values.”
Used in conjunction with human ingenuity and creativity, causal AI is on course to optimize advertising budgets, discover the best way to reach the right audience, and make a real difference—for both clients—and ultimately, patients.
To learn more about this emerging field of science consider a course on Causal AI.
Reference:
1. Mauricio G. Cohen, Gregg C. Fonarow, Eric D. Peterson, Mauro Moscucci, David Dai, Adrian F. Hernandez, Robert O. Bonow, and Sidney C. Smith, Jr. “Racial and Ethnic Differences in the Treatment of Acute Myocardial Infarction.” Circulation. 2010;121:2294–2301. https://www.ahajournals.org/doi/10.1161/circulationaha.109.922286.
