66: Multicate: Estimating and predicting Conditional Average Treatment Effects using Multiple Studies

Music City Center 

The multicate R package provides tools for estimating Conditional Average Treatment Effects (CATEs) using data from multiple studies and predicting CATEs in target populations. It supports the analysis of heterogeneous treatment effects by combining data from randomized controlled trials, observational data, or a combination of the two, as detailed in Brantner et al. (2024). The primary function, estimate_cate(), supports multiple estimation and aggregation methods, offering flexible CATE estimation using non-parametric methods adapted to handle data from multiple studies. Key features include variable importance metrics, study-specific and overall treatment effect estimates (with corresponding standard errors), and visualization options such as histograms, boxplots, and interpretation trees via plot(). Additionally, it offers covariate-specific visualizations to examine heterogeneous CATEs across studies through plot_vteffect(). The predict() function leverages the estimated CATE models to predict treatment effects in new populations. This poster will describe the multicate package and illustrate its use using data from studies of medications for depression.

Combining data

Treatment effect heterogeneity

Machine learning

Personalized medicine

Data integration

Depressive disorder 

Health Policy Statistics Section