In an era of big data, the ability to distinguish between mere correlation and true causation is arguably the most critical skill for researchers and data scientists. This talk attempts to provide a conceptual introduction to the modern framework of causal inference. We will explore how to move from “seeing” to “doing” by using Directed Acyclic Graphs (DAGs) to model the causal structure of the world. We will learn how to identify causal effects, apply the Backdoor Criterion to mitigate the biases of confounding, and also address the challenge of “transportability”—taking findings from one context and applying them to another.
