48 citations · 242 across the 64 of their papers we have counts for
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Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
Alicia Curth, Mihaela van der Schaar
The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on…
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding
Alexis Bellot, Mihaela van der Schaar
Unobserved confounding is one of the greatest challenges for causal discovery. The case in which unobserved variables have a widespread effect on many of the observed ones is parti…
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…
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
Changhee Lee, Mihaela van der Schaar
Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, su…