Quasi-experimental evaluation estimates the causal impact of an intervention based on observational data. The approach differs from a randomized controlled experiment in that the subjects are not randomly assigned between a treatment and a control group. Hence, the issue is to find a proper comparison group, the so-called counterfactual, that resembles the treatment group in everything but the fact of receiving the intervention (Sect. 14.1). Several methods exist. The differences in differences method uses panel data and calculates the effect of an intervention by comparing the changes in outcome over time between the treated and non-treated groups (Sect. 14.2). Propensity score matching relies on the estimation of scores (probability of participating in the treatment) to select and pair subjects with similar characteristics. The impact is then computed as the difference in means between the two selected groups (Sect. 14.3). Regression discontinuity design compares subjects in the vicinity of a cutoff point around which the intervention is dispensed. The underlying assumption is that subjects lying closely on either side of the threshold share similar characteristics (Sect. 14.4). Last, instrumental variable estimation addresses the problem of endogeneity in individual participation (Sect. 14.5). Examples are provided all along this chapter, with detailed R-CRAN programs, to provide the reader with a complete description of these approaches.
KeywordsDifferences in differences Propensity score matching Regression discontinuity Instrumental variables
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