Estimating and Using Propensity Scores with Partially Missing Data

By R. D'Agostino, Donald B. Rubin. 2000

316 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/18c21be851546efdd366cfe184dbe5d614b7e492

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ian R. White
2. S. Seaman
3. M. H. Clark
4. Peter M. Steiner
5. W. Shadish
6. Georgia Salanti

In Statistics and Probability

1. S. Seaman
2. Ian R. White
3. M. H. Clark
4. Peter M. Steiner
5. W. Shadish
6. Georgia Salanti
7. A. Chaimani
8. K. Tilling

Citations in the corpus

• Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments (2008, methodology): “Hence we moved to more current missing data methods, but those results may also prove sensitive to which current method is used (D’Agostino and Rubin 2000).”
• Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness (2014, methodology): “D’Agostino and Rubin proposed instead modeling the joint distribution of X , T , and R, where T = 1 if the individual is assigned to active treatment and T = 0 if assigned to control, and R denotes the missingness pattern of X , i.e., it denotes which predictors of propensity are observed. The model”