1.2k citations · 5k across the 152 of their papers we have counts for
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A robust approach to quantifying uncertainty in matching problems of causal inference
Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin
Unquantified sources of uncertainty in observational causal analyses can break the integrity of the results. One would never want another analyst to repeat a calculation with the s…
MALTS: Matching After Learning to Stretch
Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to p…
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
Aaron Fisher, Cynthia Rudin, Francesca Dominici
Variable importance (VI) tools describe how much covariates contribute to a prediction model's accuracy. However, important variables for one well-performing model (for example, a…