1 resultado para Attention bias
em Dalarna University College Electronic Archive
Filtro por publicador
- Abertay Research Collections - Abertay University’s repository (3)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (5)
- Aquatic Commons (8)
- Archive of European Integration (2)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (4)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (15)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (9)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (4)
- Biblioteca Valenciana Digital - Ministerio de Educación, Cultura y Deporte - Valencia - Espanha (2)
- Bibloteca do Senado Federal do Brasil (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (165)
- Boston University Digital Common (11)
- Brock University, Canada (15)
- Bucknell University Digital Commons - Pensilvania - USA (5)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (31)
- CentAUR: Central Archive University of Reading - UK (104)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (19)
- Cochin University of Science & Technology (CUSAT), India (5)
- Collection Of Biostatistics Research Archive (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (10)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (108)
- Digital Archives@Colby (1)
- Digital Commons - Michigan Tech (2)
- Digital Commons - Montana Tech (2)
- Digital Commons @ DU | University of Denver Research (2)
- Digital Commons at Florida International University (2)
- DigitalCommons@The Texas Medical Center (12)
- DigitalCommons@University of Nebraska - Lincoln (2)
- Digitale Sammlungen - Goethe-Universität Frankfurt am Main (3)
- Diposit Digital de la UB - Universidade de Barcelona (2)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (12)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (4)
- Glasgow Theses Service (2)
- Helda - Digital Repository of University of Helsinki (6)
- Indian Institute of Science - Bangalore - Índia (25)
- Massachusetts Institute of Technology (5)
- Memoria Académica - FaHCE, UNLP - Argentina (3)
- Ministerio de Cultura, Spain (14)
- National Center for Biotechnology Information - NCBI (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Portal de Revistas Científicas Complutenses - Espanha (2)
- Publishing Network for Geoscientific & Environmental Data (3)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (92)
- Queensland University of Technology - ePrints Archive (124)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (3)
- Repositório digital da Fundação Getúlio Vargas - FGV (15)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (27)
- Research Open Access Repository of the University of East London. (2)
- SAPIENTIA - Universidade do Algarve - Portugal (2)
- School of Medicine, Washington University, United States (3)
- Universidad del Rosario, Colombia (4)
- Universidad Politécnica de Madrid (14)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universitat de Girona, Spain (2)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (8)
- Université de Lausanne, Switzerland (6)
- Université de Montréal, Canada (34)
- University of Connecticut - USA (5)
- University of Queensland eSpace - Australia (1)
- University of Southampton, United Kingdom (1)
- WestminsterResearch - UK (2)
Resumo:
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.