In this paper we introduce %CEM, a macro package allowing researchers to automatically perform coarsened exact matching (CEM) in SAS environment. CEM is a non-parametric matching method widely used by researchers to avoid the confounding influence of pre-treatment control variables to improve causal inference in quasi-experimental studies. %CEM introduces a completely automated process which allows SAS users to efficiently perform CEM in fields in which large data sets are common and where SAS is the most popular statistical tool. In addition, such a macro may be used to test several coarsening combinations of numeric variables. This option also provides a visual representation of thematching frontier, thus enabling researchers to select the optimal setting which takes into account both the (Formula presented.) imbalance and the percentage of matched units. The paper concludes with an empirical application comparing computational performance and results obtained using alternative available software (SAS, R and STATA) using multiple administrative data sets from a large regional database.

%CEM: a SAS macro to perform coarsened exact matching / P. Berta, M. Bossi, S. Verzillo. - In: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. - ISSN 0094-9655. - (2016 Jul), pp. 1-12. [Epub ahead of print] [10.1080/00949655.2016.1203433]

%CEM: a SAS macro to perform coarsened exact matching

S. Verzillo
2016

Abstract

In this paper we introduce %CEM, a macro package allowing researchers to automatically perform coarsened exact matching (CEM) in SAS environment. CEM is a non-parametric matching method widely used by researchers to avoid the confounding influence of pre-treatment control variables to improve causal inference in quasi-experimental studies. %CEM introduces a completely automated process which allows SAS users to efficiently perform CEM in fields in which large data sets are common and where SAS is the most popular statistical tool. In addition, such a macro may be used to test several coarsening combinations of numeric variables. This option also provides a visual representation of thematching frontier, thus enabling researchers to select the optimal setting which takes into account both the (Formula presented.) imbalance and the percentage of matched units. The paper concludes with an empirical application comparing computational performance and results obtained using alternative available software (SAS, R and STATA) using multiple administrative data sets from a large regional database.
causal inference; Coarsened exact matching; matching frontier; SAS
Settore SECS-S/01 - Statistica
Settore SECS-S/03 - Statistica Economica
lug-2016
lug-2016
Centro di Ricerca Interuniversitario sui Servizi di Pubblica Utilita alla Persona (CRISP)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/411997
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