Rachael Meager
 | This article possibly contains original research. (March 2020) |
Rachael Meager | |
|---|---|
| Citizenship | Australia, Ireland, South Africa[1] |
| Alma mater | The University of Melbourne |
| Known for | Bayesian hierarchical modeling |
| Scientific career | |
| Fields | Statistics, Economics |
| Institutions | London School of Economics |
| Influences | Esther Duflo, Abhijit Banerjee |
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[]
- David Finch International Fellowship (MIT)
- AG Whitlam Honours in Economics Prize (University of Melbourne)
- BITSS SSMART Grant or Schultz Fund Grant (MIT)
References[]
- ^ Rachael Meager CV, London School of Economics
- ^ Rachael Meager google site
- ^ STICERD
- ^ "An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?". Retrieved 18 June 2021.
- ^ "Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments".
- ^ "Evidence aggregation in development economics via Bayesian hierarchical models". Retrieved 18 June 2021.
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