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Machine Learning as a Data Driven Approach to Automate Multivariate Matching Methods

Schmidt, Sebastian LU (2018) NEKP01 20181
Department of Economics
Abstract (Swedish)
This paper introduces machine learning to automate the coarsening choices in coarsened exact matching (CEM) as a monotonic imbalance bounding matching class. I suggest to replace the otherwise arbitrary multivariate stratification process with a binary classification tree. This way, I can minimise potential bias caused by subjective preferences. By using the LaLonde (1986) dataset, I systematically compare this novel approach with competing matching specifications, in particular arbitrary CEM and propensity score matching (PSM).
While the automated CEM returns more accurate results than PSM, coarsening arbitrarily performs best in terms of reducing imbalance as well as in the post-matching causal estimation.
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author
Schmidt, Sebastian LU
supervisor
organization
course
NEKP01 20181
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Matching, Machine learning, Coarsened exact matching, Causal inference
language
English
id
8949149
date added to LUP
2018-07-03 13:36:37
date last changed
2018-07-03 13:36:37
@misc{8949149,
  abstract     = {This paper introduces machine learning to automate the coarsening choices in coarsened exact matching (CEM) as a monotonic imbalance bounding matching class. I suggest to replace the otherwise arbitrary multivariate stratification process with a binary classification tree. This way, I can minimise potential bias caused by subjective preferences. By using the LaLonde (1986) dataset, I systematically compare this novel approach with competing matching specifications, in particular arbitrary CEM and propensity score matching (PSM).
While the automated CEM returns more accurate results than PSM, coarsening arbitrarily performs best in terms of reducing imbalance as well as in the post-matching causal estimation.},
  author       = {Schmidt, Sebastian},
  keyword      = {Matching,Machine learning,Coarsened exact matching,Causal inference},
  language     = {eng},
  note         = {Student Paper},
  title        = {Machine Learning as a Data Driven Approach to Automate Multivariate Matching Methods},
  year         = {2018},
}