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Inverse probability weighted estimation for general missing data problems
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DOI：10.1016/j.jeconom.2007.02.002.png)
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Abstract
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I study inverse probability weighted M-estimation under a general missing data scheme. Examples include M-estimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling with observed retention 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 a general characterization of a double robustness result due to Scharfstein et al. [1999. Rejoinder. Journal of the American Statistical Association 94, 1135-1146]. (c) 2007 Elsevier B.V. All rights reserved.
Keywords:
inverse probability weighting
sample selection
M-estimator
censored duration
average treatment effect
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