apm: Averaged Prediction Models

Introduction

The apm package implements Averaged Prediction Models (APM), a Bayesian model averaging approach for controlled pre-post designs. These designs compare differences over time between a group that becomes exposed (treated group) and one that remains unexposed (comparison group). With appropriate causal assumptions, they can identify the causal effect of the exposure/treatment.

In APM, we specify a collection of models that predict untreated outcomes. Our causal identifying assumption is that the model’s prediction errors would be equal (in expectation) in the treated and comparison groups in the absence of the exposure. This is a generalization of familiar methods like Difference-in-Differences (DiD) and Comparative Interrupted Time Series (CITS).

Because many models may be plausible for this prediction task, we combine them using Bayesian model averaging. We weight each model by its robustness to violations of the causal assumption.

Installation

To install the development version from GitHub, use:

# Install devtools if not already installed
install.packages("remotes")

# Install apm package from GitHub if not already installed
remotes::install_github("tl2624/apm")

See vignette("apm") for details on using the package.