Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment

Ben Jann

No 35, University of Bern Social Sciences Working Papers from University of Bern, Department of Social Sciences

Abstract: 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.

Keywords: influence function; sampling variance; standard error; generalized method of moments; maximum likelihood; logistic regression; inverse-probability weighting; inverse-probability weighted regression adjustment; exact matching; Mahalanobis distance matching; entropy balancing; average treatment effect; causal inference (search for similar items in EconPapers)
JEL-codes: C01 C12 C13 C21 C25 C31 C83 C87 (search for similar items in EconPapers)
Pages: 68 pages
Date: 2020-03-28, Revised 2020-08-31
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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