Estimating Causal Effects in Experiments with Network Spillovers

This accessible Handbook provides a comprehensive exploration and overview of computational social science (CSS), bringing together global experts to examine its boundary-spanning nature. With an interdisciplinary focus, chapters shed light on the transformative power of computational approaches in social science and take a forward-looking approach to anticipate the next wave of innovations in CSS including those driven by generative AI and large language models.

Covering diverse topics such as social networks, political behavior, human mobility, and collective intelligence, the Handbook highlights the vast potential of CSS to analyze and understand complex social phenomena. It explores a wide range of methodological approaches, from agent-based modeling and big data analytics to experimental designs and machine learning applications.

Designed for students, researchers, and professionals, the Handbook is an invaluable resource for those working and studying in the field of CSS. Its practical insights into the methodologies that have shaped CSS will greatly benefit social science instructors, educators, and industry professionals in social data science and analytics.