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Saraswata Chaudhuri on Muck Rack

Saraswata Chaudhuri

As seen in: MDPI, Taylor & Francis Online
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Articles

Weak Identification Robust Tests for Subvectors Using Implied Probabilities

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By Saraswata Chaudhuri, Marine Carrasco
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Heavy tail robust estimation and inference for average treatment effects

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By Jonathan Hill, Saraswata Chaudhuri
| Taylor & Francis Online
Abstract. We study the probability tail properties of Inverse probability weighting (IPW) estimators of the average treatment effect (ATE) when there is limited overlap between the covariate distributions of the treatment and control groups. Under unconfoundedness of treatment assignment conditional on covariates, such limited overlap is manifested in the propensity score for certain units being very close (but not equal) to 0 or 1.
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