Data Science Brown Bag: temporal generalization

Causal generalization is essential to contemporary political science practice. Recent methodological advances in causal generalization pays insufficient attention to issues which arise from generalization over time. One cause of this oversight is the lack of casual time series data. We identify two such time series – representing two quite distinct types of social phenomena – and demonstrate in each case how standard approaches to statistical analysis lead to inferential errors. To begin to address the issue of temporal generalization, we derive novel statistical bounds of the growing uncertainty of a given causal estimate into the future. Once implicit and untenable assumptions about unchanging covariate distribution and conditional treatment effects are made explicit and relaxed, the value of individual research designs which make weaker/fewer assumptions becomes unclear. We discuss implications for research practice, and argue that richer descriptive knowledge is essential for reducing the uncertainty from temporal causal generalization.

About the speaker:
Dr. Kevin Munger is Assistant Professor and Chair of Computational Social Science at the European University Institute in Florence. He employs cutting-edge computational and experimental methods to explore how social media and the internet shape political information and communication. His work has appeared in top-tier journals such as Nature, American Journal of Political Science, and Journal of Communication, and he is the author of two books: Generation Gap: Why the Baby Boomers Still Dominate American Politics and Culture and The YouTube Apparatus (Cambridge University Press). In 2021, he co-founded and serves as co-editor of the Journal of Quantitative Description: Digital Media. His current research spans topics like TikTok, Twitch, and the philosophy of social science, bringing a deep analytical lens to digital-era democracy.

Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it. 

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The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!). 

The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.