() and Mathias Lundin
Maria Karlsson: Department of statistics, USBE, Umeå University, Postal: Umeå, Sweden
Mathias Lundin: Department of statistics, USBE, Umeå University, Postal: Umeå, Sweden
Abstract: Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be satisfied. One requirement is that the outcomes of individuals are not affected by the treatment given to other individuals, i.e., that the no-interference assumption is satisfied. This assumption might, in many situations, not be plausible. However, recent progress in the researchfield has provided us with statistical methods for causal inference even under interference. In this paper, we review some of the most important contributions made. We also discuss how we Think these methods can or cannot be used within the eld of policy evaluation and if there are some measures to be taken when planning an evaluation study in order to be able to use a particular method. In addition, we give examples on how interference has been dealt with in some evaluation applications including, but not limited to, labor market evaluations, in the recent past.
Keywords: causal effect; causal inference; contagion effect; direct and indirect effects; evaluation studies; neighborhood effect; peer effect; peer influence effect; policy intervention; spillover effect; SUTVA; treatment effect
33 pages, December 19, 2016
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