Part 1:Foundations of Causal Inference
This part introduces the core principles of causal inference, focusing on distinguishing causation from association and correlation. It covers fundamental concepts such as confounding variables, biases, and assumptions in causal analysis, providing a solid theoretical base. Additionally, it introduces the use of R for basic causal inference, preparing you for practical applications using R.
This part has the following chapters:
- Chapter 1, Introducing Causal Inference
- Chapter 2, Unraveling Confounding and Associations
- Chapter 3, Initiating R with a Basic Causal Inference Example
