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pro vyhledávání: '"HAZLETT, CHAD"'
The Gaussian Process (GP) is a highly flexible non-linear regression approach that provides a principled approach to handling our uncertainty over predicted (counterfactual) values. It does so by computing a posterior distribution over predicted poin
Externí odkaz:
http://arxiv.org/abs/2407.10442
Autor:
Hazlett, Chad, Shinkre, Tanvi
Researchers have long run regressions of an outcome variable (Y) on a treatment (D) and covariates (X) to estimate treatment effects. Even absent unobserved confounding, the regression coefficient on D in this setup reports a conditional variance wei
Externí odkaz:
http://arxiv.org/abs/2403.03299
Autor:
Rohde, Adam, Hazlett, Chad
In the quest to make defensible causal claims from observational data, it is sometimes possible to leverage information from "placebo treatments" and "placebo outcomes". Existing approaches employing such information focus largely on point identifica
Externí odkaz:
http://arxiv.org/abs/2310.15266
Autor:
Faries, Douglas, Gao, Chenyin, Zhang, Xiang, Hazlett, Chad, Stamey, James, Yang, Shu, Ding, Peng, Shan, Mingyang, Sheffield, Kristin, Dreyer, Nancy
The assumption of no unmeasured confounders is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains underutilized. The lack of use is likely in
Externí odkaz:
http://arxiv.org/abs/2309.07273
With the precipitous decline in response rates, researchers and pollsters have been left with highly non-representative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioner
Externí odkaz:
http://arxiv.org/abs/2107.08075
Akademický článek
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Autor:
Hazlett, Chad J, Hainmueller, Jens
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 153-156).
This dissertation focuses on the challenges of mak
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 153-156).
This dissertation focuses on the challenges of mak
Externí odkaz:
http://hdl.handle.net/1721.1/92080
Akademický článek
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K zobrazení výsledku je třeba se přihlásit.
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Autor:
Hazlett, Chad1, Parente, Francesca2
Publikováno v:
Journal of Politics. Jul2023, Vol. 85 Issue 3, p1145-1150. 6p.
Autor:
Hazlett, Chad
Publikováno v:
Statistica Sinica, 2020 Jan 01. 30(3), 1155-1189.
Externí odkaz:
https://www.jstor.org/stable/26968924