2 citations · 2 across the 3 of their papers we have counts for
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Causal Machine Learning for Cost-Effective Allocation of Development Aid
Milan Kuzmanovic, Dennis Frauen, Tobias Hatt +1
The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by 'leaving no one behind', and, to achieve the SDGs by 2030, poor countries r…
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE es…
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies
Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that al…