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Title: thinkCausal: A tool to help researchers learn while they do Authors:  Jennifer Hill - New York University (United States) [presenting]
George Perrett - NYU (United States)
Abstract: Causal inference is a necessary tool in education research for answering pressing and ever-evolving questions around policy and practice. Increasingly researchers are using more complicated machine learning algorithms to estimate causal effects. These methods take some of the guesswork out of analyses, decrease the opportunity for $p$-hacking, and are often better suited for more fine-tuned causal inference tasks such as identifying varying treatment effects and generalizing results from one population to another. However, these more sophisticated methods are more difficult to understand and are often only accessible in more technical, less user-friendly software packages. The thinkCausal project is working to address these challenges by developing a highly-scaffolded, multi-purpose causal inference software package in R Shiny with the BART predictive algorithm as a foundation. The software will scaffold the researcher through the data analytic process and provide options to access technology-based teaching tools to understand foundational concepts in causal inference and machine learning. What we have accomplished will be outlined, and the challenges and opportunities in building this type of tool will be discussed.