A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark
Résumé
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, the authors compare estimates generated by matching to an experimental benchmark. The enormous sample size enables the authors to exactly match each treated subject to 40 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.