Abstract: The Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social, behavioral, biomedical, and statistical sciences. In this design, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold---and withheld otherwise. In this talk, I will discuss the assumptions under which the RD design can be used to learn about treatment effects, and how to make valid…

  Abstract: Data and algorithms are ubiquitous in all scientific, industrial and personal domains. Data now come in multiple forms (text, image, video, web, sensors, etc.), are massive, and require more and more complex processing beyond their mere indexation or the computation of simple statistics, such as recognizing objects in images or translating texts. For all of these tasks, commonly referred to as artificial intelligence (AI), significant recent progress has allowed algorithms to reach performances that were deemed unreachable a…

We live in an increasingly polarized present, looking to a complex and uncertain future while basing our legislative decisions on systems of the past. We need the processes and structures that underpin our political decision-making to be aligned with the complexities of the 21st century. Such changes cannot be undertaken by a technocratic elite, potentially disenfranchising citizens further from their governing institutions. Rather, political institutions must seek to improve transparency, openness, and accountability. The great divide between science and policy…

Abstract In the study of war, a recurring observation is that conflict between two opponents is shaped by third parties. The actions of these parties are in turn influenced by other proximate players. These considerations lead us to propose a model of conflict in a network. We study the influence of resources, technology, and the network of connections on the dynamics of war and the prospects of peace. Bio Sanjeev Goyal is Professor of Economics at the University of Cambridge…

 Abstract The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: “computational social science.” In this talk I highlight two areas of research that would not have been possible just a handful of years ago: first, using “big data” to study social contagion on networks; and second, using virtual labs to extend the scale, duration, and…

Abstract: This talk will review a series of recent papers that develop new methods based on machine learning methods to approach problems of causal inference, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments, environments with unconfoundedness, instrumental variables, and panel data will be considered. Bio: Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford,…

  Abstract I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding, to me at least, was tied to…