Causal inference with observational data

Problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are described: panel regression, matching or reweighting, instrumental variables, and regression discontinuity. Practical examples are offered, and discussion focuses on checking required assumptions to the extent possible.


Issue Date:
2007
Publication Type:
Journal Article
DOI and Other Identifiers:
st0136 (Other)
PURL Identifier:
http://purl.umn.edu/119292
Published in:
Stata Journal, Volume 07, Number 4
Page range:
507-541
Total Pages:
35

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 Record created 2017-04-01, last modified 2017-08-26

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