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Session Submission Type: Created Panel
This panel focuses on overcoming challenges in causal and descriptive inference when units affect other units. The papers collectively advance the methodological frontier by addressing how social ties and spatial spillovers complicate issues of measurement and the estimation of treatment effects. They feature approaches that study information diffusion over social networks, featuring re-randomization, new experimental designs, and novel machine learning techniques that measure and capture effects of group and individual interactions.
Rerandomization Methods to Minimize Interference in Experiments - Ryan T. Moore, American University
Quantifying Narrative Reuse across Languages - Hannah Waight, New York University; Megan Brown, University of Michigan; Jason Greenfield, New York University; Kevin Aslett, University of Central Florida; Margaret E Roberts, University of California, San Diego; Anton Shirikov, University of Kansas; Jonathan Nagler, New York University; Joshua A. Tucker, New York University; Solomon Messing, New York University
Design-Based Inference for Group Interaction Experiments - Jiawei Fu, New York University; Cyrus Samii, New York University; Ye Wang, University of North Carolina, Chapel Hill
Spillover Effects, Causal Inference, and the Ecological Inference - Jongwoo Jeong, Washington University in St.Louis; Young-An Kim, Florida State University