6 citations · 7 across the 5 of their papers we have counts for
5 papers
A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
Graham Tierney, Srikar Katta, Christopher Bail +2
Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry review…
Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect
Jon Donnelly, Srikar Katta, Emanuele Borgonovo +1
Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipeline, an analyst estimates VI for a…
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova +7
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many rea…
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
Srikar Katta, Harsh Parikh, Cynthia Rudin +1
Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these…
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
Jon Donnelly, Srikar Katta, Cynthia Rudin +1
Quantifying variable importance is essential for answering high-stakes questions in fields like genetics, public policy, and medicine. Current methods generally calculate variable…