Skip navigation
Use este identificador para citar ou linkar para este item: https://repositorio.ufpe.br/handle/123456789/45286

Compartilhe esta página

Título: Causal inference in sampling from finite populations
Autor(es): NÓBREGA, Rafael Zimmerle da
Palavras-chave: Estatística aplicada; Estudos observacionais; Amostragem balanceada; Calibração
Data do documento: 18-Fev-2022
Editor: Universidade Federal de Pernambuco
Citação: NÓBREGA, Rafael Zimmerle da. Causal inference in sampling from finite populations. 2022. Dissertação (Mestrado em Estatística) - Universidade Federal de Pernambuco, Recife, 2022.
Abstract: Causal inference deals with estimating the effects of specific interventions on a response variable. The estimation strategy involves comparing units exposed to intervention factor’s levels, forming a treatment group, with those units not exposed, forming a control group. The control group serves as the base to estimate the counterfactual response of the treatment group. In observational studies, a major concern when building such groups is to ensure their comparability, controlling for characteristics others than the treatment itself, that may cause undesired interference on causal effects estimates, leading to systematic bias. Although the theory behind observational studies has advanced with methods to reduce such bias using conditional inference, in several of these studies data is obtained through complex probability sampling designs seldom taken into account in the estimation process. This thesis considers that, beyond representing a source of variability that must be incorporated in the analysis, sample design and estimation techniques can have a central role to estimate causal effects efficiently. Studies are carried out to investigate the use of balanced samples to ensure compa- rability between treatment and control groups with respect to the distributions of covariates, and the use of calibration estimates for the control group average response, improving es- timates of the average counterfactual treatment response. The methods are compared with those already available in the literature, via Monte Carlo simulation.
URI: https://repositorio.ufpe.br/handle/123456789/45286
Aparece nas coleções:Dissertações de Mestrado - Estatística

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
DISSERTAÇÃO Rafael Zimmerle da Nóbrega.pdf615,71 kBAdobe PDFThumbnail
Visualizar/Abrir


Este arquivo é protegido por direitos autorais



Este item está licenciada sob uma Licença Creative Commons Creative Commons