Robustness to Parametric Assumptions in Missing Data Models

  title={Robustness to Parametric Assumptions in Missing Data Models},
  author={Bryan Graham},
Suppose we have a random sample from a population of interest. For each sampled unit we observe the covariate X, which we assume is discrete with support { x 1 , ... , x K }. For some units, we also observe the variable Y. Let D = 1 if we observe Y, and D = 0 otherwise. We are interested in the population mean of Y, θ = 피[ Y ] = ∑ k=1 K p k μ k , where μ k = 피[Y | X = x k ] and p k = Pr (X = x k ). We assume that Y is missing at random (MAR): Y ⊥ D | X. Suppose also that the propensity score e… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
3 Citations
10 References
Similar Papers


Publications citing this paper.


Publications referenced by this paper.
Showing 1-10 of 10 references

Recent Developments in the Econometrics of Program Evaluation.

  • S David, David Card
  • Journal of Economic Literature,
  • 2008
Highly Influential
4 Excerpts

Regres - sion Discontinuity Inference with Specification Error

  • N Carl
  • Journal of Econometrics
  • 2008

When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects.

  • Angrist, Joshua, Jinyong Hahn
  • Review of Economics and Statistics,
  • 2004

On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects

  • W Guido
  • Econometrica
  • 1998

Estimation of Regression Coefficients When Some Regressors Are Not Always Observed

  • R Paul
  • Journal of the American Statistical Association
  • 1994

“ Model - based Direct Adjustment

  • M Jeffrey
  • Journal of the American Statistical Association
  • 1987

“ Parametric Empirical Bayes Inference : Theory and Applications

  • M. James, Andrea Rotnitzky, Lue Ping Zhao
  • Journal of the American Statistical Association
  • 1983

Similar Papers

Loading similar papers…