Likelihood estimate of treatment effects under selection bias
2012 (English)Report (Other academic)
We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.
Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna , 2012. , 11 p.
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2012:06
Causal inference, h-likelihood, shared random-effect model, summer job effect
Probability Theory and Statistics Economics
Research subject Complex Systems – Microdata Analysis, General Microdata Analysis - methods
IdentifiersURN: urn:nbn:se:du-11318OAI: oai:DiVA.org:du-11318DiVA: diva2:571932