Inverse probability weighted estimation for general missing data problems

Jeffrey Wooldridge

No CWP05/04, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 double robustness 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.

JEL-codes: C13 C21 C23 (search for similar items in EconPapers)
Pages: 41 pp.
Date: 2004-04-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (10)

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Related works:
Journal Article: Inverse probability weighted estimation for general missing data problems (2007)
Working Paper: Inverse probability weighted estimation for general missing data problems (2004)
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