Causal Inference for Data Science
This teaching line focuses on moving from predictive models to causal questions: what would happen under a different intervention, policy, design, or institutional decision?
Topics include directed acyclic graphs, confounding, matching, fixed effects, difference-in-differences, regression discontinuity, instrumental variables, sensitivity analysis, and causal machine learning.
Cristian Candia
Associate Professor, Data Science Institute, School of Engineering, Universidad del Desarrollo, Chile. Head of CRiSS-LAB.
Cristian Candia studies how societies transform information into collective relevance through attention, memory, preferences, and coordination. His work combines computational social science, network science, AI, and large-scale behavioral data to understand how groups, institutions, and societies decide what matters.