Jann, Ben (2020). Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment (University of Bern Social Sciences Working Papers 35). Bern: University of Bern, Department of Social Sciences
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In Jann (2019) I provided some reflections on influence functions for linear regression (with an application to regression adjustment). Based on an analogy to variance estimation in the generalized method of moments (GMM), I extend the discussion in this paper to maximum-likelihood models such as logistic regression and then provide influence functions for a variety of treatment effect estimators such as inverse-probability weighting (IPW), regression adjustment (RA), inverse-probability weighted regression adjustment (IPWRA), exact matching (EM), Mahalanobis distance matching (MD), and entropy balancing (EB). The goal of this exercise is to provide a framework for standard error estimation in all these estimators.
Item Type: |
Working Paper |
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Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Sociology |
UniBE Contributor: |
Jann, Ben |
Subjects: |
300 Social sciences, sociology & anthropology |
Series: |
University of Bern Social Sciences Working Papers |
Publisher: |
University of Bern, Department of Social Sciences |
Language: |
English |
Submitter: |
Ben Jann |
Date Deposited: |
07 May 2020 15:05 |
Last Modified: |
05 Dec 2022 15:37 |
JEL Classification: |
C01, C12, C13, C21, C25, C31, C83, C87 |
BORIS DOI: |
10.7892/boris.142529 |
URI: |
https://boris.unibe.ch/id/eprint/142529 |
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