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To Impute or not to Impute? Missing Data in Treatment Effect Estimation

To Impute or not to Impute? Missing Data in Treatment Effect Estimation

4 February 2022
Jeroen Berrevoets
F. Imrie
T. Kyono
James Jordon
M. Schaar
ArXivPDFHTML

Papers citing "To Impute or not to Impute? Missing Data in Treatment Effect Estimation"

5 / 5 papers shown
Title
Dynamic Inter-treatment Information Sharing for Individualized Treatment
  Effects Estimation
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
V. Chauhan
Jiandong Zhou
Ghadeer O. Ghosheh
Soheila Molaei
David A. Clifton
30
8
0
25 May 2023
Combining Observational and Randomized Data for Estimating Heterogeneous
  Treatment Effects
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt
Jeroen Berrevoets
Alicia Curth
Stefan Feuerriegel
M. Schaar
CML
52
29
0
25 Feb 2022
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Trent Kyono
Yao Zhang
Alexis Bellot
M. Schaar
CML
47
61
0
04 Nov 2021
Learning Representations for Counterfactual Inference
Learning Representations for Counterfactual Inference
Fredrik D. Johansson
Uri Shalit
David Sontag
CML
OOD
BDL
232
719
0
12 May 2016
MissForest - nonparametric missing value imputation for mixed-type data
MissForest - nonparametric missing value imputation for mixed-type data
D. Stekhoven
Peter Buhlmann
177
4,222
0
04 May 2011
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