Brady Neal — accepted AI-conference papers

2024

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

ICLR 2024spotlight

We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation. Unlike machine learning, there is no perfect analogue of cross-validation for model selection as we do not observe the counterfactual potential outcomes. Towards this…

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2020

In search of robust measures of generalization

Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang +2

NeurIPS 2020poster

One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now trains networks to achieve small training error also leads to small error on held-out data from the same population. It is widely appreciated that some worst-case…

Cited by 122 Source ↗PDF ↗code ↗Scholar ↗