Sensitivity analysis of the unconfoundedness assumption in observational studies

In observational studies, the estimation of a treatment effect on an outcome of interest is often done by controlling on a set of pre-treatment characteristics (covariates). This yields an unbiased estimator of the treatment effect when the assumption of unconfoundedness holds, that is, there are no unobserved covariates affecting both the treatment assignment and the outcome. This is in general not realistically testable. It is, therefore, important to conduct an analysis about how sensitive the inference is with respect to the unconfoundedness assumption. In this paper we propose a procedure to conduct such a Bayesian sensitivity analysis, where the usual parameter uncertainty and the uncertainty due to the unconfoundedness assumption can be compared. To measure departures from the assumption we use a correlation coefficient which is intuitively comprehensible and ensures that the results of sensitivity analyses made on different evaluation studies are comparable. Our procedure is applied to the Lalonde data and to a study of the effect of college choice on income in Sweden.Causal inference; effects of college choice; propensity score; register data

Разработка инвестиционного проекта «Пасека» в развитии агротуризма

Материалы V Междунар. науч. конф. студентов, аспирантов и молодых ученых, Гомель, 24 мая 2012

Geophysical and atmospheric evolution of habitable planets

The evolution of Earth-like habitable planets is a complex process that depends on the geodynamical and geophysical environments. In particular, it is necessary that plate tectonics remain active over billions of years. These geophysically active environments are strongly coupled to a planet's host star parameters, such as mass, luminosity and activity, orbit location of the habitable zone, and the planet's initial water inventory. Depending on the host star's radiation and particle flux evolution, the composition in the thermosphere, and the availability of an active magnetic dynamo, the atmospheres of Earth-like planets within their habitable zones are differently affected due to thermal and nonthermal escape processes. For some planets, strong atmospheric escape could even effect the stability of the atmosphere

Sensitivity Analysis of Untestable Assumptions in Causal Inference

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

Hammondorgeln i musikundervisning : En intervjustudie

Det har dykt upp en mängd nya s.k. Hammondkloner på marknaden i försök att digitalt återskapa Hammondsoundet. Syftet med denna studie är att utforska om Hammondorgeln har ersatts av digitala Hammondkloner i musikutbildning på högskola, folkhögskola och inom privatundervisningen i Sverige. Studien genomfördes metodiskt med tre kvalitativa forskningsintervjuer och observation av två privatundervisningar i Hammondorgel. Resultatet är att det går att utbilda sig i att spela Hammondorgel B3 med Leslie 122 på högskola och inom privatundervisning i Sverige och att Hammondorgeln ännu inte har ersatts helt av Hammondkloner i undervisning i att spela Hammondorgel. Det är däremot ovisst huruvida man kan utbilda sig att spela riktig Hammondorgel på folkhögskola av det som framgår utav min forskning

Sensitivity Analysis of Untestable Assumptions in Causal Inference

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

Forecasting in the fast-moving consumer goods sector : A study of forecasting techniques’ capability of accurate promotion forecasts.

Prognostisering anses vara en nyckelprocess som påverkar alla delar av en verksamhet och genom att effektivt applicera prognoser kan ett flertal fördelar erhållas, däribland ökad tillgänglighet av produkter till konsumenter och minskade lagernivåer genom hela leveranskedjan. Inom dagligvaruhandeln, med dess snabba varuomsättning, skiftande konsumentbehov samt varierande produkthållbarheter är det särskilt nödvändigt att ständigt ligga steget före.ICA Sverige AB är idag den dominerande aktören inom svensk dagligvaruhandel med knappt hälften av marknadsandelarna. Likt många andra större koncerner tillämpar ICA Sverige AB prognoser för att förutse framtida försäljningsvolymer. Vad som skiljer sig är att verksamheten inte endast applicerar prognoser mot konsumenter utan även på försäljningen mot ICA-handlarna. Ett viktigt affärsområde för verksamheten är de kampanjer som dels bedrivs mot konsumenter och dels, i första hand, mot ICA-handlarna. Verksamhetens nuvarande process för kampanjprognostisering innefattar manuell planering och uppskattning av framtida kampanjers omfattning, vilket är extremt svårt.Denna studie syftar till att undersöka ett antal olika prognostiseringstekniker, från områdena informationsutvinning (eng. data mining) och maskininlärning, och deras förmåga att skapa träffsäkra kampanjprognoser. I studien jämförs även prognostiseringsteknikernas prestanda mot ICA:s befintliga manuella kampanjprognoser.Studiens resultat visar att det är möjligt att uppnå en hög träffsäkerhet på kampanjprognoser för dagligvaruhandeln med hjälp av prognostiseringstekniker. Flertalet av de studerade prognostiseringsteknikerna uppnådde dessutom en högre träffsäkerhet än ICA:s befintliga manuella kampanjprognoser.Program: Systemarkitekturutbildninge

On statistical methods for labor market evaluation under interference between units

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 satised. 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 research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field 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 within some evaluation applications including, but not limited to, labor market evaluations, in the recent past

On statistical methods for labor market evaluation under interference between units

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 satised. 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 research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field 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 within some evaluation applications including, but not limited to, labor market evaluations, in the recent past