Democratizing Methods

Music City Center 

The past few decades have seen an explosion in the development of freely available software to implement statistical methods and algorithms to help us explore and analyze data. However researchers tend to assume that once they have made their software available (e.g. through CRAN or on GitHub) that their job is done. Typically, very little attention is paid to ensuring that the software is easy to use or that it is likely to be used correctly. Even less attention is paid to helping researchers new to a method to understand the underlying assumption or how to appropriate interpret the output. In this talk I will describe a new software tool for causal inference that works to scaffold the user experience to maximize the probability that researchers can use the tool appropriately and understand the foundational ideas. Moreover, I'll describe a randomized experiment we performed to understand whether this tool actually accomplishes these goals relative to traditional software. I conclude with calls to action for those that develop methods.

software

accessibility

causal inference

machine learning

BART

R