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Sensitivity analysis of the unconfoundedess assumption in observational studies
Umeå universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.
Umeå universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.
2009 (Engelska)Rapport (Övrigt vetenskapligt)
Ort, förlag, år, upplaga, sidor
Uppsala: Institute of Labour Market Policy Evaluation , 2009.
Serie
IFAU Working Papers, ISSN 1651-1166 ; 2009:12
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-12752OAI: oai:DiVA.org:umu-12752DiVA, id: diva2:152423
Tillgänglig från: 2007-04-23 Skapad: 2007-04-23 Senast uppdaterad: 2018-06-09Bibliografiskt granskad
Ingår i avhandling
1. Sensitivity Analysis of Untestable Assumptions in Causal Inference
Öppna denna publikation i ny flik eller fönster >>Sensitivity Analysis of Untestable Assumptions in Causal Inference
2011 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

This thesis contributes to the research field of causal inference, where the effect of a treatment on an outcome is of interest is concerned. Many such effects cannot be estimated through randomised experiments. For example, the effect of higher education on future income needs to be estimated using observational data. In the estimation, assumptions are made to make individuals that get higher education comparable with those not getting higher education, to make the effect estimable. Another assumption often made in causal inference (both in randomised an nonrandomised studies) is that the treatment received by one individual has no effect on the outcome of others. If this assumption is not met, the meaning of the causal effect of the treatment may be unclear.

In the first paper the effect of college choice on income is investigated using Swedish register data, by comparing graduates from old and new Swedish universities. A semiparametric method of estimation is used, thereby relaxing functional assumptions for the data.

One assumption often made in causal inference in observational studies is that individuals in different treatment groups are comparable, given that a set of pretreatment variables have been adjusted for in the analysis. This so called unconfoundedness assumption is in principle not possible to test and, therefore, in the second paper we propose a Bayesian sensitivity analysis of the unconfoundedness assumption. This analysis is then performed on the results from the first paper.

In the third paper of the thesis, we study profile likelihood as a tool for semiparametric estimation of a causal effect of a treatment. A semiparametric version of the Bayesian sensitivity analysis of the unconfoundedness assumption proposed in Paper II is also performed using profile likelihood.

The last paper of the thesis is concerned with the estimation of direct and indirect causal effects of a treatment where interference between units is present, i.e., where the treatment of one individual affects the outcome of other individuals. We give unbiased estimators of these direct and indirect effects for situations where treatment probabilities vary between individuals. We also illustrate in a simulation study how direct and indirect causal effects can be estimated when treatment probabilities need to be estimated using background information on individuals.

Ort, förlag, år, upplaga, sidor
Umeå: Statistiska institutionen, Umeå universitet, 2011. s. 19
Serie
Statistical studies, ISSN 1100-8989 ; 45
Nyckelord
Observational studies, semiparametric regression, unconfoundedness, Causal inference
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
statistik
Identifikatorer
urn:nbn:se:umu:diva-43239 (URN)978-91-7459-222-1 (ISBN)
Disputation
2011-05-20, Samhällsvetarhuset, Hörsal D, Umeå universitet, Umeå, 10:15 (Svenska)
Opponent
Handledare
Tillgänglig från: 2011-04-29 Skapad: 2011-04-25 Senast uppdaterad: 2018-06-08Bibliografiskt granskad

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de Luna, XavierLundin, Mathias

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