Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
By Jiayun Dong, Cynthia Rudin. 2019
29 citations recorded by the source corpus.
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and in other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and a specific variable is important to some of them and not to others? In that case, we may not be able to tell from a single well-performing model whether a variable is always important in predicting the outcome. Rather than depending on variable importance for a single predictive model, we would like to explore variable importance for all approximately-equally-accurate predictive models. This work introduces the concept of a variable importance cloud, which maps every variable to its importance for every good predictive model. We show properties of the variable importance cloud and draw connections other areas of statistics. We introduce variable importance diagrams as a projection of the variable importance cloud into two dimensions for visualization purposes. Experiments with criminal justice and marketing data illustrate how variables can change dramatically in importance for approximately-equally-accurate predictive models.
Subfields: Artificial Intelligence
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- Alexandra Chouldechova
- Flávio du Pin Calmon
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Citations in the corpus
- Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, methodology): “When defined in this manner, the set of good models is also called the “Rashomon set” (Rudin, 2019; Fisher et al., 2019; Dong & Rudin, 2020; Semenova et al., 2020).”
- Predictive Multiplicity in Classification (2019, methodology): “Fisher et al. (2018) and Dong & Rudin (2019) develop methods to measure variable importance over the set of competing models.”
- Model Multiplicity: Opportunities, Concerns, and Solutions (2022, methodology): “are perceived as a normatively objectionable basis for decisionmaking, proceduralmultiplicity suggests—and research has demonstrated empirically [9, 26]—thatmodel developers can remove these from theirmodelswhile still potentially achieving the same level of accuracy in their predictions.”