Andrew Heiss • 3/21/2024

Demystifying causal inference estimands: ATE, ATT, and ATU

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This technical article delves into causal inference, specifically explaining the Average Treatment Effect (ATE), Average Treatment Effect on the Treated (ATT), and Average Treatment Effect on the Untreated (ATU). It discusses the potential outcomes framework, contrasts it with DAG-based approaches, and provides a practical guide for calculating these estimands using observational data, R, and techniques like inverse probability weighting.

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#statistics #R #Tidyverse
Demystifying causal inference estimands: ATE, ATT, and ATU

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