02603cam a22002777 4500001000600000003000500006005001700011008004100028100002300069245012000092260006600212490005100278500002000329520133600349530006101685538007201746538003601818690011001854690005501964690008402019700002102103710004202124830008602166856003702252856003602289t0343NBER20161210021054.0161210s2007 mau||||fs|||| 000 0 eng d1 aBhattacharya, Jay.10aDo Instrumental Variables Belong in Propensity Scores?h[electronic resource] /cJay Bhattacharya, William B. Vogt. aCambridge, Mass.bNational Bureau of Economic Researchc2007.1 aNBER technical working paper seriesvno. t0343 aSeptember 2007.3 aPropensity score matching is a popular way to make causal inferences about a binary treatment in observational data. The validity of these methods depends on which variables are used to predict the propensity score. We ask: "Absent strong ignorability, what would be the effect of including an instrumental variable in the predictor set of a propensity score matching estimator?" In the case of linear adjustment, using an instrumental variable as a predictor variable for the propensity score yields greater inconsistency than the naive estimator. This additional inconsistency is increasing in the predictive power of the instrument. In the case of stratification, with a strong instrument, propensity score matching yields greater inconsistency than the naive estimator. Since the propensity score matching estimator with the instrument in the predictor set is both more biased and more variable than the naive estimator, it is conceivable that the confidence intervals for the matching estimator would have greater coverage rates. In a Monte Carlo simulation, we show that this need not be the case. Our results are further illustrated with two empirical examples: one, the Tennessee STAR experiment, with a strong instrument and the other, the Connors' (1996) Swan-Ganz catheterization dataset, with a weak instrument. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web. 7aC1 - Econometric and Statistical Methods and Methodology: General2Journal of Economic Literature class. 7aI1 - Health2Journal of Economic Literature class. 7aI2 - Education and Research Institutions2Journal of Economic Literature class.1 aVogt, William B.2 aNational Bureau of Economic Research. 0aTechnical Working Paper Series (National Bureau of Economic Research)vno. t0343.4 uhttp://www.nber.org/papers/t034341uhttp://dx.doi.org/10.3386/t0343