Título:
Causal inference
Autor Pessoal:
Data de Publicação:
2023
GVPL_PUBLICATION_INFO:
Cambridge, Massachusetts : The MIT Press, [2023]
Descrição Física:
203 pages : illuatrations ; 18 cm.
ISBN:
9780262545198
Resumo:
Causality is central to the understanding and use of data; without an understanding of cause and effect relationships, we cannot use data to answer important questions in medicine and many other fields.
Conteúdos:
The effects caused by treatments -- Randomized experiments -- Observational studies : the problem -- Adjustments for measured covariates -- Sensitivity to unmeasured covariates -- Quasi-experimental devices in the design of observational studies -- Natural experiments, discontinuities, and instruments -- Replication, resolution and evidence factors -- Uncertainty and complexity in causal inference -- Postscript: Key ideas, chapter by chapter.
Linguagem:
Inglês
