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December 2016 Maximizing the information content of a balanced matched sample in a study of the economic performance of green buildings
Cinar Kilcioglu, José R. Zubizarreta
Ann. Appl. Stat. 10(4): 1997-2020 (December 2016). DOI: 10.1214/16-AOAS962


Buildings have a major impact on the environment through excessive use of resources, such as energy and water, and large carbon dioxide emissions. In this paper we revisit a previously published study about the economics of environmentally sustainable buildings and estimate the effect of green building practices on market rents. For this, we use new matching methods that take advantage of the clustered structure of the buildings data. We propose a general framework for matching in observational studies and specific matching methods within this framework that simultaneously achieve three goals: (i) maximize the information content of a matched sample (and, in some cases, also minimize the variance of a difference-in-means effect estimator); (ii) form the matches using a flexible matching structure (such as a one-to-many/many-to-one structure); and (iii) directly attain covariate balance as specified—before matching—by the investigator. To our knowledge, existing matching methods are only able to achieve, at most, two of these goals simultaneously. Also, unlike most matching methods, the proposed methods do not require estimation of the propensity score or other dimensionality reduction techniques, although with the proposed methods these can be used as additional balancing covariates in the context of (iii). Using these matching methods, we find that green buildings have 3.3% higher rental rates per square foot than otherwise similar buildings without green ratings—a moderately larger effect than the one found by the prior study.


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Cinar Kilcioglu. José R. Zubizarreta. "Maximizing the information content of a balanced matched sample in a study of the economic performance of green buildings." Ann. Appl. Stat. 10 (4) 1997 - 2020, December 2016.


Received: 1 June 2015; Revised: 1 June 2016; Published: December 2016
First available in Project Euclid: 5 January 2017

zbMATH: 06688766
MathSciNet: MR3592046
Digital Object Identifier: 10.1214/16-AOAS962

Keywords: Causal inference , matched sampling , observational studies , propensity score

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 4 • December 2016
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