New chapter in the Causal Book is out: IV the Bayesian Way. In this chapter we examine the price elasticity of demand for cigarettes and identify the causal treatment effect using state taxes as an instrument. We’ll streamline the conceptual model and data across chapters later.
Basically, the sample question here is: What is the effect of a price increase on smoking? As always, the solution includes complete code and data. This chapter uses the powerful RStan and CmdStanR via brms and ulam, and, unlike the other chapters, doesn’t replicate the solution in Python (due to the added computational cost of the sampling process).
Causal Book is an interactive resource that presents a network of concepts and methods for causal inference. Due to the nonlinear – network structure of the book, each new chapter comes with a number of other linked sections and pages. All of this added content can be viewed in the graph view (available only on desktop in the upper right corner).
This book aims to be a curated set of design patterns for causal inference and the application of each pattern using a variety of methods in three approaches: Statistics (narrowly defined), Machine Learning, and Bayesian. Each design pattern is supported by business cases that use the pattern. Three approaches are compared using the same data and model. The book discusses the lesser known and understood details of the modeling process in each pattern.