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Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect
Pennsylvania State University.
Pennsylvania State University.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0003-3187-1987
2019 (English)In: Statistica sinica, ISSN 1017-0405, E-ISSN 1996-8507Article in journal (Refereed) Epub ahead of print
Abstract [en]

When estimating the treatment effect in an observational study, we use a semi- parametric locally efficient dimension reduction approach to assess both the treat- ment assignment mechanism and the average responses in both treated and non- treated groups. We then integrate all results through imputation, inverse prob- ability weighting and double robust augmentation estimators. Double robust estimators are locally efficient while imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator to automatically combine the two, which re- tains the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estima- tors through simulated experiments and a real dataset concerning the effect of maternal smoking on baby birth weight.

Place, publisher, year, edition, pages
Taipei: Academia Sinica, Institute of Statistical Science , 2019.
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-163592DOI: 10.5705/ss.202018.0416OAI: oai:DiVA.org:umu-163592DiVA, id: diva2:1355217
Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2019-10-03

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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