Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

• Published in: Journal of the American Statistical Association Vol. 95; no. 449; pp. 77 - 84
• Main Authors: Horowitz, Joel L., Manski, Charles F.
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis Group 01.03.2000; American Statistical Association
• Subjects: Attrition; Bounds; Clinical trials; Confidence interval; Covariance; Data analysis; Estimation; Exact sciences and technology; Family structure; Health outcomes; Hypertension; Identification; Inference; Interval estimators; Mathematical economics; Mathematics; Missing data; Nonparametric inference; Point estimators; Population dynamics; Population estimates; Probability and statistics; Sampling theory, sample surveys; Sciences and techniques of general use; Secondary schools; Statistics; Theory and Methods; Missing data; Clinical trial; Non parametric estimation; Sampling
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2000.10473902

Analysis of randomized experiments with missing covariate and outcome data is problematic, because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This article shows how population parameters can be bounded without making untestable distributional assumptions. Bounds are also derived under the assumption that covariate data are missing completely at random. In each case the bounds are sharp; they exhaust all of the information available given the data and the maintained assumptions. The bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school.