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Inverse probability weighted estimation for general missing data problems

Authors: Jeffrey M. Wooldridge
Date: 01 December 2007
Type: Journal Article, Journal of Econometrics, Vol. 141, No. 2, pp. 1281-1301

Abstract

I study inverse probability weighted M-estimation under a general missing data scheme. The cases covered that do not previously appear in the literature include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect for linear exponential family quasi-log-likelihood functions, and variable probability sampling with observed retainment frequencies. I extend an important result known to hold in special cases: estimating the selection probabilities is generally more efficient than if the known selection probabilities could be used in estimation. For the treatment effect case, the setup allows for a simple characterization of a モdouble robustnessヤ result due to Scharfstein, Rotnitzky, and Robins (1999): given appropriate choices for the conditional mean function and quasi-log-likelihood function, only one of the conditional mean or selection probability needs to be correctly specified in order to consistently estimate the average treatment effect.

Previous version:
Jeffrey M. Wooldridge April 2004, Inverse probability weighted estimation for general missing data problems, cemmap Working Paper, CWP05/04

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