Abstract: Decision making is central to all aspects of society, private and public. Consequently, using data and statistics to improve decision-making has a rich history, perhaps best exemplified by the randomized experiment. In practice, however, experiments carry significant risk. For example, making an online recommendation system worse could result in millions of lost profits; selecting an inappropriate treatment for a patient could have devastating consequences. Luckily, organizations like hospitals and companies who serve recommendations routinely collect vast troves of observational data on decisions and outcomes. In this talk, I discuss how to make the best use of such data to improve policy, starting with an example of what can go wrong if we’re not careful. Then, I present two pieces of research on how to avoid such perils if we are willing to say more about less.
Organiser: Umberto Picchini (email@example.com). Please contact me if you wish to be informed of future seminars.