31 citations · 47 across the 5 of their papers we have counts for
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Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs
Alicia Curth
The sudden appearance of modern machine learning (ML) phenomena like double descent and benign overfitting may leave many classically trained statisticians feeling uneasy -- these…
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt, Jeroen Berrevoets, Alicia Curth +2
Estimating heterogeneous treatment effects is an important problem across many domains. In order to accurately estimate such treatment effects, one typically relies on data from ob…
On Inductive Biases for Heterogeneous Treatment Effect Estimation
Alicia Curth, Mihaela van der Schaar
We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treat…
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Alicia Curth, Mihaela van der Schaar
The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a…