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Using high frequency pre-treatment outcomes to identify causal effects in non-experimental data: Causal effects in non-experimental data
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0002-1260-7737
2018 (English)Report (Other academic)
Abstract [en]

In observational studies it is common to use matching strategies to consistently estimate the average treatment effect of the treated (ATET) under the unconfoundedness assumption of the outcome and the treatment assignment mechanism. Matching is often based on a set of time invariant covariates together with one or a few pre-treatment measurements of the outcome. This paper proposes estimation strategies using a large number of pre-treatment measurements of the outcome to consistently estimate the average treatment effect of the treated (ATET). The assumptions under which these approaches are valid are given. It is shown when and how the strategies can be used to replace, or add to, time-invariant covariates to identify and consistently estimate the ATET. The theoretical results and estimation strategies are illustrated by a study of electricity consumption.

Place, publisher, year, edition, pages
2018. , p. 34
Series
Working paper / Department of Statistics, Uppsala University ; 2018:1
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-355260OAI: oai:DiVA.org:uu-355260DiVA, id: diva2:1228196
Available from: 2018-06-27 Created: 2018-06-27 Last updated: 2018-06-29Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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