About CODE@MITThe newly emerging capability to rapidly deploy and iterate micro-level, in-vivo, randomized experiments in complex social and economic settings at population scale is, in our view, one of the most significant innovations in modern social science. As more and more social interactions, behaviors, decisions, opinions and transactions are digitized and mediated by online platforms, our ability to quickly answer nuanced causal questions about the role of social behavior in population-level outcomes such as health, voting, political mobilization, consumer demand, information sharing, product rating and opinion aggregation is becoming unprecedented. This new toolkit portends a sea-change in our scientific understanding of human behavior and dramatic improvements in social and business policy as a result. When appropriately theorized and rigorously applied, randomized experiments are the gold standard of causal inference and a cornerstone of effective policy. But the scale and complexity of these experiments also create scientific and statistical challenges for design and inference. Different disciplines are approaching causal inference in contrasting, complementary ways. The purpose of the Conference on Digital Experimentation at MIT (CODE) is to bring together leading researchers conducting and analyzing large scale randomized experiments in digitally mediated social and economic environments, in various scientific disciplines including economics, computer science and sociology, in order to lay the foundation for ongoing relationships and to build a lasting multidisciplinary research community.
- CODE@MIT Organizers: Sinan Aral, Dean Eckles, John Horton, Alex 'Sandy' Pentland
- 2021 CODE@MIT Technical Program Committee: David Holtz (chair), Jennifer Allen, Emma van Inwegen, Yuan Yuan, Cathy Cao, Alex Moehring
- 2021 Session Chairs: Paramveer Dhillon, Dean Eckles, Dave Holtz, Emma van Inwegen, Alex Moehring, Zanele Munyikwa, Alex 'Sandy' Pentland, Hong-Yi Tu Ye, Yuan Yuan