31 citations · 47 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Estimating Structural Target Functions using Machine Learning and Influence Functions
Alicia Curth, Ahmed M. Alaa, Mihaela van der Schaar
We aim to construct a class of learning algorithms that are of practical value to applied researchers in fields such as biostatistics, epidemiology and econometrics, where the need…