Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Yinjun Wu, Mayank Keoliya, Kan Chen +7
Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical sett…
cs.LG2023
Rectifying Group Irregularities in Explanations for Distribution Shift
Adam Stein, Yinjun Wu, Eric Wong +1
It is well-known that real-world changes constituting distribution shift adversely affect model performance. How to characterize those changes in an interpretable manner is poorly…