Chief Scientist | Booz Allen Hamilton
The analytics community has invested significant resources in developing effective predictive analytical methods. However, even the most accurate predictive forecasts have limited value unless they can also provide clear action steps to bring about desired results. In other words, the cause of the data is more important than the data itself. Alex will introduce causal inference in the context of Bayesian Belief Networks (BBNs). BBNs produce accurate predictive forecasts, but with appropriate modeler input are also able to identify causal relationships between variables and pinpoint drivers of desired targets. With causal relationships identified, BBNs may be used in a prescriptive fashion in order to make actionable decisions. Alex will dive into a case study in the aviation space which identifies causal drivers of daily flight operations on flight delays and allows us to prescribe delay-reduction plans by acting on controllable drivers.
Alex Cosmas is a Chief Scientist in Booz Allen Hamilton’s Strategic Innovation Group, specializing in predictive analytics across the transportation, travel, and consumer sectors. He is a recognized expert in the use of probabilistic and causal models to perform both deductive and inductive reasoning from large datasets. He has consulted for Fortune 100’s both domestically and internationally in the areas of demand modeling, consumer choice, network modeling, revenue management and pricing. He earned his B.S.in Applied Physics from Columbia Engineering and M.S. degrees in Technology Policy and Aerospace Engineering, both from the Massachusetts Institute of Technology.