James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details:
Here’s the course outline:
How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an MBA affect my earnings? How much might an increase in minimum wages affect employment levels?
These are examples of causal questions. Sadly, they are the sorts of questions that data scientists’ run-of-the-mill predictive models can be ill-equipped to answer.
In this one-day course, we will cover methods for answering these questions, using easy-to-use Bayesian data analysis tools. The topics include:
– Why do experiments work? Understanding the Rubin causal model
– Regularized GLMs; bad controls; souping-up linear models to capture nonlinearities
– Using panel data to control for some types of unobserved confounding information
– ITT, natural experiments, and instrumental variables
– If we have time, using machine learning models for causal inference.
All work will be done in R, using the new rstanarm package.
Lunch, coffee, snacks and materials will be provided. Attendees should bring a laptop with R, RStudio and rstanarm already installed. A limited number of scholarships are available. The course is in no way affiliated with Columbia.