Comparing potential outcomes and directed acyclic graph approaches to understanding causal inference from data.

Music City Center 

Most beginning statistics students are warned that "Correlation is not causation." Yet many of the most important statistical problems involve using data to make causal inferences. This paper shows how to build student understanding of important principles underlying causal inference. The approach involves comparing the potential outcomes approach pioneered by Donald Rubin with the Directed Acyclic Graphs (DAGs) approach pioneered by Judea Pearl. Central to the discussion is the idea of identification. Another key point is understanding the difference between the data collection and measurement process and the statistical modeling process used to perform causal inference.

causal inference