By Barbara Schneider
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Additional info for Estimating Causal Effects: Using Experimental and Observation Designs
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Students at the ends of the spectrum mentioned above) have virtually no probability of attending private schools on the low end or public schools on the high end. Because these students have no matches, they are excluded from analyses. 31 Propensity scores address an important issue in empirical research, namely, estimates of effects for certain groups when randomization is not possible, and where sample elements have self-selected themselves into treatment or control conditions. All statistical methods, from the simplest regressions to the most complex structural models, have elements of this limitation when used to analyze phenomena with heterogeneous responses.
In addition, instrumental variables may be only weakly related to the endogenous variable; the use of such “weak” instruments can result in biased and misleading estimates (Currie; see also Staiger and Stock, 1997, and Bound, Jaeger, and Baker, 1995, for a discussion of weak instruments; Angrist and Krueger, 1995, show that the estimates in their 1991 article are not affected by this problem). Estimating Causal Effects Using Observational Data 49 Propensity Scores A third method used to correct for selection bias is propensity scores.
Perhaps the most common way is to sort students from each group into “bins” or strata based on the distribution of propensity scores. Within each bin, the characteristics of students in the two treatment conditions are similar on a weighted composite of observed covariates. , public and private schools) is achieved. , students at the ends of the spectrum mentioned above) have virtually no probability of attending private schools on the low end or public schools on the high end. Because these students have no matches, they are excluded from analyses.