Heterogeneous Effects Analysis of Conjoint Experiments

A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) . This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) , to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

cjbart

Overview

cjbart is an R package for analyzing conjoint experiments using Bayesian Additive Regression Trees (BART), specifically focusing on inspecting heterogeneous treatment effects.

This package is in its early stages of development, and core functionality is liable to change.

Installation

The latest development version of cjbart can be installed directly from this repository, using the following code:

# install.packages("devtools")
devtools::install_github("tsrobinson/cjbart")

Getting help

If you come across any issues, or have any suggestions for improvements, please raise an issue here.