Fig. 2: Matching algorithms reduce observational bias.
From: Housing policies and energy efficiency spillovers in low and moderate income communities
The relative performance of genetic matching and PSM in standardized percent bias. The key conditioning and testing variables shown include property, demographic and neighbourhood characteristics. The conditioning variables are identified with asterisks and include observable property characteristics (average baseline consumption, property size and age, number of beds and baths). To mitigate the effect of possible unobservables on energy use, the market value of the property was added to the set of matching variables as a proxy for unobserved quality attributes. Genetic matching achieved 90.6% bias reduction, while PSM achieved 77.8% bias reduction; therefore, the remaining bias in standardized percent bias is −9.6% and −22.2%, respectively. Although both methods substantially reduce median bias and offer a high degree of covariate balance, the genetic matching algorithm is preferred over PSM.
