Bayesian Logistic Regression for Decision-making

Cutting through the noise in multivariate space

Note: In this paid post, I'm going to introduce a useful program evaluation method, explain when you would use it, work an example, and give you code you can copy to do the analysis yourself. I do paid posts for a couple of reasons: 1) because certain posts are highly technical and take me a long time to write, and 2) I want to support Substack in its current form as an ad-free platform.

Why Use Bayesian Logistic Regression in Formative Evaluation?

Formative evaluation often requires making data-driven decisions using partial information. We want to optimize program outcomes while the program is running. This is different from summative evaluation, in which we determine the value of the program post facto.

A Bayesian logistic regression model is a powerful tool in this context because it provides posterior distributions over the coefficients in a regression predicting a binary outcome, allowing us to create probabilistic decision rules that maximize the chances of achieving the desired outcome. It can do this for multiple inputs, so that we can see how different inputs stack up against one another and interact in multivariate space.