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Causal Inference for Sociology
Mon, August 10, 4:00 to 5:30pm, TBASession Submission Type: Paper Session (90 minute)
Description
This session invites submissions on causal inference methodology relevant to sociology. Papers that develop novel causal methods are particularly welcome. Topics include, but are not limited to, causal inference for network data, causal machine learning, the intersection of large language models and causal inference, and causal mediation analysis. Scholars from other disciplines are also encouraged to submit methodological work applicable to sociological questions.
Sub Unit
Presider
Individual Presentations
Regime-Dependent Distributional Effects: A Causal Framework for Aggregate Counterfactuals - Kevin Kiley, North Carolina State University
Explaining Association with RIF Regression - Lai Wei, The University of Hong Kong
Truly descriptive research with models - Ian Lundberg, University of California, Los Angeles; Kristin Liao, University of California-Los Angeles
Beyond Selection Bias and Missing Data: Recovering True Prevalences - Max Thaning, Stockholm University