A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark

Author(s): 

Kevin Arceneaux, Alan S. Gerber, and Donald P. Green

ISPS ID: 
ISPS10-033
Full citation: 
Arceneaux, Kevin , Alan S. Gerber, and Donald P. Green (2010), "A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark," Sociological Methods & Research 39(2): 256-282.
Abstract: 
In recent years, social scientists have increasingly turned to matching as a method for drawing causal inferences from observational data. Matching compares those who receive a treatment to those with similar background attributes who do not receive a treatment. Researchers who use matching frequently tout its ability to reduce bias, particularly when applied to data sets that contain extensive background information. Drawing on a randomized voter mobilization experiment, we compare estimates generated by matching to an experimental benchmark. The enormous sample size enables us to exactly match each treated subject to forty untreated subjects. Matching greatly exaggerates the effectiveness of pre-election phone calls encouraging voter participation. Moreover, it can produce nonsensical results: matching suggests that another pre-election phone call that encouraged people to wear their seat belts also generated huge increases in voter turnout. This illustration suggests that caution is warranted when applying matching estimators to observational data, particularly when one is uncertain about the potential for biased inference.
Attachments: 
https://isps.yale.edu/sites/default/files/publication/2012/12/ISPS10-033.pdf
Supplemental information: 

Link to article here.

Location: 
Location details: 
IL
Publication date: 
2010
Publication type: 
Discipline: