6 citations · 12 across the 10 of their papers we have counts for
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stat.ML2018
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from…
stat.ML2018
Learning Weighted Representations for Generalization Across Designs
Fredrik D. Johansson, Nathan Kallus, Uri Shalit +1
Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to building robust and reliable machine learning applications. We focus…