Identification of causal effects: a methodological review

Detalhes bibliográficos
Autor(a) principal: Teixeira, Pedro Medeiros
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/29527
Resumo: O presente trabalho é uma revisão metodológica das duas abordagens mais comuns para lidar com questões que envolvem causalidade: os desfechos potenciais (potential outcomes) de Neyman-Rubin e os modelos de grafos de Pearl. O principal objetivo é discutir questões de identificação, esclarecendo as premissas por trás de cada abordagem, apontando divergências e, quando possível, encontrando áreas nas quais as duas estruturas são complementares. Além disso, são analisados ​​os detalhes práticos de Diferença em Diferenças e Controle Sintético, dois métodos que adotam os desfechos potenciais como estrutura e são amplamente utilizados na literatura de avaliação de políticas.
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spelling Teixeira, Pedro MedeirosEscolas::EMApStruchiner, Claudio JoséFernandes, MarceloTargino, Rodrigo dos Santos2020-08-05T13:25:28Z2020-08-05T13:25:28Z2020-06-16https://hdl.handle.net/10438/29527O presente trabalho é uma revisão metodológica das duas abordagens mais comuns para lidar com questões que envolvem causalidade: os desfechos potenciais (potential outcomes) de Neyman-Rubin e os modelos de grafos de Pearl. O principal objetivo é discutir questões de identificação, esclarecendo as premissas por trás de cada abordagem, apontando divergências e, quando possível, encontrando áreas nas quais as duas estruturas são complementares. Além disso, são analisados ​​os detalhes práticos de Diferença em Diferenças e Controle Sintético, dois métodos que adotam os desfechos potenciais como estrutura e são amplamente utilizados na literatura de avaliação de políticas.The present work is a methodological review of the two most common frameworks to address questions that involves causality: Neyman-Rubin's potential outcomes and Pearl's graphical models. The main purpose is to discuss identification issues clarifying the assumptions behind each approach, pointing out disagreements and, when feasible, finding areas where both frameworks are complementaries. Besides that, it is analysed the practical details of Difference-in-Differences and Synthetic Control, two methods that adopts the potential outcomes as the framework and are widely used in policy evaluation literature.engCausalidadeModelos estatísticosInferência CausalCausalityStatistical modelMatemáticaCausalidade - Modelos matemáticosCiências sociais - MetodologiaModelos matemáticosEstatística - AnáliseIdentification of causal effects: a methodological reviewinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALdissertacao_Pedro_submissao.pdfdissertacao_Pedro_submissao.pdfapplication/pdf1596413https://repositorio.fgv.br/bitstreams/b360dea7-d85b-452c-9b9d-ca0b67178a47/download0b9d8bd35a72e71347ffbf1ae05046b3MD53TEXTdissertacao_Pedro_submissao.pdf.txtdissertacao_Pedro_submissao.pdf.txtExtracted 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dc.title.por.fl_str_mv Identification of causal effects: a methodological review
title Identification of causal effects: a methodological review
spellingShingle Identification of causal effects: a methodological review
Teixeira, Pedro Medeiros
Causalidade
Modelos estatísticos
Inferência Causal
Causality
Statistical model
Matemática
Causalidade - Modelos matemáticos
Ciências sociais - Metodologia
Modelos matemáticos
Estatística - Análise
title_short Identification of causal effects: a methodological review
title_full Identification of causal effects: a methodological review
title_fullStr Identification of causal effects: a methodological review
title_full_unstemmed Identification of causal effects: a methodological review
title_sort Identification of causal effects: a methodological review
author Teixeira, Pedro Medeiros
author_facet Teixeira, Pedro Medeiros
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.member.none.fl_str_mv Struchiner, Claudio José
Fernandes, Marcelo
dc.contributor.author.fl_str_mv Teixeira, Pedro Medeiros
dc.contributor.advisor1.fl_str_mv Targino, Rodrigo dos Santos
contributor_str_mv Targino, Rodrigo dos Santos
dc.subject.por.fl_str_mv Causalidade
Modelos estatísticos
Inferência Causal
topic Causalidade
Modelos estatísticos
Inferência Causal
Causality
Statistical model
Matemática
Causalidade - Modelos matemáticos
Ciências sociais - Metodologia
Modelos matemáticos
Estatística - Análise
dc.subject.eng.fl_str_mv Causality
Statistical model
dc.subject.area.por.fl_str_mv Matemática
dc.subject.bibliodata.por.fl_str_mv Causalidade - Modelos matemáticos
Ciências sociais - Metodologia
Modelos matemáticos
Estatística - Análise
description O presente trabalho é uma revisão metodológica das duas abordagens mais comuns para lidar com questões que envolvem causalidade: os desfechos potenciais (potential outcomes) de Neyman-Rubin e os modelos de grafos de Pearl. O principal objetivo é discutir questões de identificação, esclarecendo as premissas por trás de cada abordagem, apontando divergências e, quando possível, encontrando áreas nas quais as duas estruturas são complementares. Além disso, são analisados ​​os detalhes práticos de Diferença em Diferenças e Controle Sintético, dois métodos que adotam os desfechos potenciais como estrutura e são amplamente utilizados na literatura de avaliação de políticas.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-08-05T13:25:28Z
dc.date.available.fl_str_mv 2020-08-05T13:25:28Z
dc.date.issued.fl_str_mv 2020-06-16
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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url https://hdl.handle.net/10438/29527
dc.language.iso.fl_str_mv eng
language eng
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