The ability to rapidly deploy and iterate on micro-level, in-vivo, randomized digital experiments in intricate socio-economic environments at a population scale has been, in our estimation, a transformational step in contemporary social science.

With more social interactions, behaviors, decisions, opinions, and transactions digitized and/or mediated by online platforms, our capacity to quickly answer complex causal questions about the impact of social behavior on macro-level outcomes such as health, voting, political mobilization, consumer demand, and information dissemination has reached unparalleled levels. This evolved toolkit is shifting our scientific comprehension of human behavior and has facilitated monumental advancements in social and business policy.

When grounded in robust theoretical models and applied with rigorous precision, digital experiments have emerged as the gold standard of causal inference and the linchpin of effective policy formulation and evaluation. Yet, the sheer scale and complexity of these experiments also pose distinctive scientific and statistical challenges, particularly in terms of experimental design and inference.

The purpose of the Conference on Digital Experimentation at MIT (CODE@MIT) is to foster an interdisciplinary forum for leading researchers engaged in the design and analysis of randomized digital experiments. This includes researchers from diverse scientific disciplines (such as economics, computer science, and sociology) and working in academia, industry, civil society, and government — all with the common objective of establishing a sustainable multidisciplinary research community that continues to push the boundaries of our understanding.

Early registration ends September 30th; more information including abstract submission is available on the conference site.

Please note: accepted authors must register for and attend the conference in-person in order to present. Thank you!

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MIT Conference on Digital Experimentation (CODE@MIT) 2024