A Leader's Guide to Causal Inference

Causal inference is the study of how actions/interventions/treatments impact an outcome. This blog post provides a high-level introduction to the two main ways of making causal claims 1) using experiments and 2) observational studies.

Introduction to Identification

Statistical inference teaches us “how” to learn from data, whereas identification analysis explains “what” we can learn from it. Although “what” logically precedes “how,” the concept of identification is less widely understood than that of estimation or inference. Since it is an important topic in causal inference, we will devote a series of posts to the topic. In this first installment, we give a general but somewhat abstract definition of identifiability. The next few posts in the series will focus on identification in the causal context.