Welcome to the 1st course in our 7 part series on causal inference concepts and methods created by Duke University with support from eBay, Inc. Designed to teach you causal inference concepts, methods, and how to code in R with realistic data, this introduction focuses on how to interpret treatment effects, and how to explore and derive key summary statistics from dataframes. We’ll stay away from dense statistical math and focus instead on higher level concepts that data scientists need to always consider when examining and making inferences about data. The course instructors and creators are Dr. Matt Masten (Duke University), James Speckart (Duke), Brian Aronson (Duke), Dr. Tyler Ransom (University of Oklahoma), Dr. Bentley Coffey (University of South Carolina), and Alexandra Cooper (Duke).
Getting Started With The Basics
This chapter will introduce you to the basic concepts behind causal inference, and will let you learn and practice through R
Introduction to Treatment Effects
This chapter will introduce you to individual, group, and average treatment effects, and will let you learn and practice through R
Confounders, Counterfactuals, and p-Hacking
This chapter will introduce you the important issues of confounders, counterfactuals, and the problem of p-hacking, and will let you learn and practice through R