85 citations · 206 across the 12 of their papers we have counts for
5 papers · 1 filter
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
David S. Watson, Joshua O'Hara, Niek Tax +2
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast,…
Intervention Generalization: A View from Factor Graph Models
Gecia Bravo-Hermsdorff, David S. Watson, Jialin Yu +2
One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapp…
Conditional Feature Importance for Mixed Data
Kristin Blesch, David S. Watson, Marvin N. Wright
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical pers…
Adversarial random forests for density estimation and generative modeling
David S. Watson, Kristin Blesch, Jan Kapar +1
We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests. Inspired by generative adversarial networks, we implement a recursiv…
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh, Jakob Zeitler, David Watson +3
Causal effect estimation is important for many tasks in the natural and social sciences. We design algorithms for the continuous partial identification problem: bounding the effect…