Location: E52 Samberg Conference Center
Event site
On October 14-15, we hosted nearly 60 presentations, including plenary presentations (listed below), parallel presentations, a fireside panel, and poster slam featuring early-stage work.
Estimation and Evaluation of Optimal Policies. Susan Athey (Stanford University)
Escaping from Government and Corporate Surveillance. Evidence from the MIT Digital Currency Experiment. Catherine Tucker (MIT)
When Randomized Experiments are Plentiful. Dean Eckles (MIT)
Insights from Behavioral Economics for Consumer Finance Markets. Antoinette Schoar (MIT)
Machine Learning, Causal Inference, and Estimating Heterogeneous Treatment Effects. Jas Sekhon (UC Berkeley)
Machine Learning Choices. Johan Ugander (Stanford University)
Optimal Design of Experiments on Social Networks. Edo Airoldi (Harvard University)
Trustworthy Results: Pitfalls in Online Controlled Experiments. Ron Kohavi (Microsoft)
Full program available here.
Image above from Fireside Panel, "The Tyranny of Algorithms?" courtesy of Sita Magnuson of dpict; more images available here. Read the blog here.
About the event: The 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) - designed for an academic-focused audience - 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.