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Deals with the use of a catheter threaded into the carotid artery to prevent stroke or relieve pre-stroke symptoms, followed by the placement of a stent. This book also covers intracranial as well as extracranial procedures.

Carotid artery --- Stents (Surgery) --- Surgery. --- Stenosis. --- Stent (Surgery) --- Stints (Surgery) --- Surgical stents --- Bandages and bandaging --- Surgical instruments and apparatus --- Carotid stenosis --- Stenosis --- Diseases --- Cardiology. --- Neurology. --- Neurosurgery. --- Medicine --- Nervous system --- Neuropsychiatry --- Heart --- Internal medicine --- Nerves --- Neurosurgery --- Surgery --- Neurology .

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This chapter provides a selective review of some contemporary approaches to program evaluation. One motivation for our review is the recent emergence and increasing use of a particular kind of "program" in applied microeconomic research, the so-called Regression Discontinuity (RD) Design of Thistlethwaite and Campbell (1960).  We organize our discussion of these various research designs by how they secure internal validity:  in this view, the RD design can been seen as a close "cousin" of the randomized experiment. An important distinction which emerges from our discussion of  "heterogeneous treatment effects" is between ex post (descriptive) and ex ante (predictive) evaluations; these two types of evaluations have distinct, but complementary goals.  A second important distinction we make is between statistical statements that are descriptions of our knowledge of the program assignment process and statistical statements that are structural assumptions about individual behavior. Using these distinctions, we examine some commonly employed evaluation strategies, and assess them with a common set of criteria for "internal validity", the foremost goal of an ex post evaluation.  In some cases, we also provide some concrete illustrations of how internally valid causal estimates can be supplemented with specific structural assumptions to address "external validity": the estimate from an internally valid "experimental" estimate  can be viewed as a "leading term" in an extrapolation for a parameter of interest in an ex ante evaluation.

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Using data on more than 27,000 establishments (1983-1999) in the United States, this paper produces estimates of the causal effect of unionization of employer closure by exploiting the fact that most employers become 'unionized' as a partial consequence of a secret ballot election among the workers. If employers where unions barely won the election (e.g. by one vote) are ex ante comparable in all other ways to employers where unions barely lost (by one vote), differences in their subsequent outcomes should represent the true impact of union recognition. The regression discontinuity analysis finds little or no union effect on short- and long-run employer survival rates over 1- to 18-year horizons. We thus conclude that evidence of large effects of unions would more likely be found 1) along the within-employer (intensive margin) of employment and/or 2) in analyses of union threat effects.

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This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally-funded job training programs in the United States. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied micro-econometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution, are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is non-random sample selection/attrition.

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A regression discontinuity (RD) research design is appropriate for program evaluation problems in which treatment status (or the probability of treatment) depends on whether an observed covariate exceeds a fixed threshold. In many applications the treatment-determining covariate is discrete. This makes it impossible to compare outcomes for observations "just above" and "just below" the treatment threshold, and requires the researcher to choose a functional form for the relationship between the treatment variable and the outcomes of interest. We propose a simple econometric procedure to account for uncertainty in the choice of functional form for RD designs with discrete support. In particular, we model deviations of the true regression function from a given approximating function -- the specification errors -- as random. Conventional standard errors ignore the group structure induced by specification errors and tend to overstate the precision of the estimated program impacts. The proposed inference procedure that allows for specification error also has a natural interpretation within a Bayesian framework.