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Rachael Meager
CitizenshipAustralia, Ireland, South Africa[1]
Alma materThe University of Melbourne, Massachusetts Institute of Technology[1]
Known forBayesian hierarchical modeling
Scientific career
FieldsStatistics, Economics
InstitutionsLondon School of Economics

Rachael Meager is an Australian economist and statistician.[2] She currently holds an Assistant Professorship at the London School of Economics, within the STICERD (Suntory and Toyota International Centres for Economics and Related Disciplines) research centre.[3]

Meager has significantly contributed to the development of advanced statistical methods in economics. She has advanced the application of Bayesian Analysis for Randomized Controlled Trial data, for instance investigating robustness effects against treatment heterogeneity by considering the impact of leaving out small fractions of the data.[4] She applies these techniques to development topics such as the impact of microcredit on economic growth and inequality.[5]

Education

Meager earned a Bachelor of Arts from the University of Melbourne as well as a Bachelor of Honours in Economics. She received her Ph.D. from MIT Massachusetts Institute of Technology on evidence aggregation in Bayesian hierarchical modeling.[6] Her principal adviser was Esther Duflo.

Grants and awards

References

  1. ^ a b Rachael Meager CV, London School of Economics
  2. ^ Rachael Meager google site
  3. ^ STICERD
  4. ^ Broderick, Tamara; Giordano, Ryan; Meager, Rachael (2020). "An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?". arXiv:2011.14999 [stat.ME].
  5. ^ Meager, Rachael (2019). "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments" (PDF). American Economic Journal: Applied Economics. 11: 57–91. doi:10.1257/app.20170299. S2CID 158524203.
  6. ^ Meager, Rachael (2017). Evidence aggregation in development economics via Bayesian hierarchical models (Thesis). Massachusetts Institute of Technology. hdl:1721.1/111358. Retrieved 18 June 2021.


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