Showing papers by "Donald B. Rubin published in 2009"

Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

Abstract: Government-sponsored job-training programs must be subject to evaluation to assess whether their effectiveness justifies their cost to the public. The evaluation usually focuses on employment and total earnings, although the effect on wages is also of interest, because this effect reflects the increase in human capital due to the training program, whereas the effect on total earnings may be simply reflecting the increased likelihood of employment without any effect on wage rates. Estimating the effects of training programs on wages is complicated by the fact that, even in a randomized experiment, wages are “truncated” (or less accurately “censored”) by nonemployment, that is, they are only observed and well-defined for individuals who are employed. In this article, we develop a likelihood-based approach to estimate the wage effect of the US federally-funded Job Corps training program using “Principal Stratification”. Our estimands are formulated in terms of: (1) the effect of the training program on wages...

Public Schools Versus Private Schools: Causal Inference With Partial Compliance

Abstract: An approach to handle partial compliance behavior using principal stratification is presented and applied to a subset of the longitudinal data from the New York City School Choice Scholarship Program, a randomized experiment designed to assess the effects of private schools versus public schools on academic achievement. The initial analysis suggests an interesting relationship between compliance with the offer and academic achievement, including a possible “beneficial rejected offer” effect and a possible “adjustment hardship” effect. These results seem to favor public schools in the sense they suggest that the collection of students who would attend private school when offered the scholarship but attend public school without the offer had a lower average posttest score if they attended private school than if they attended public school. This case study illustrates the strengths of principal stratification: the explicit examination of specific assumptions and directly interpretable results with possible p...

Testing treatment effects in unconfounded studies under model misspecification: Logistic regression, discretization, and their combination

Abstract: Logistic regression is commonly used to test for treatment effects in observational studies. If the distribution of a continuous covariate differs between treated and control populations, logistic regression yields an invalid hypothesis test even in an uncounfounded study if the link is not logistic. This flaw is not corrected by the commonly used technique of discretizing the covariate into intervals. A valid test can be obtained by discretization followed by regression adjustment within each interval.