  Observational health data can be leveraged to measure the real-world use and
potential benefits or risks of existing medical interventions. However, lack of
programming proficiency and advanced knowledge of causal inference methods
excludes some clinicians and non-computational researchers from performing such
analyses. Code-free dashboard tools provide accessible means to estimate and
visualize treatment effects from observational health data. We present
TreatmentEstimatoR, an R Shiny dashboard that facilitates the estimation of
treatment effects from observational data without any programming knowledge
required. The dashboard provides effect estimates from multiple algorithms
simultaneously and accommodates binary, continuous, and time-to-event outcomes.
TreatmentEstimatoR allows for flexible covariate selection for treatment and
outcome models, comprehensive model performance metrics, and an exploratory
data analysis tool. TreatmentEstimatoR is available at
https://github.com/CollinSakal/TreatmentEstimatoR. We provide full installation
instructions and detailed vignettes for how to best use the dashboard.
