Making Good Policies with Bad Causal Inference: The Role of Prediction and Machine Learning

Abstract:
In the last few decades, we have learned to be careful about causation, and have developed powerful tools for making causal inferences from data. Applying these tools has generated both policy impact and conceptual insights. Dr. Mullainathan will argue that there are a large class of problems where causal inference is largely unnecessary where, instead, prediction is the central challenge. These problems are ideally suited to machine learning and high dimensional data analysis tools. In this talk Dr. Mullainathan will (1) try to delineate the difference between problems that require causation and problems that require prediction; (2) describe results from solving one such prediction problem in detail; (3) highlight the set of new statistical issues these problems raise; and (4) argue that solving these problems can also generate both policy impact and conceptual insights.