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stat.ML2022★ 4 cited
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…
stat.ML2022★ 9 cited
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
Jeroen Berrevoets, Fergus Imrie, Trent Kyono +2
Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missin…