The potential outcome framework for causal inference is fundamentally a missing data problem with a special, the so-called file-matching, pattern of missing data. Given the large body of literature on various methods for handling missing data and associated software, it will be useful to use such methods to facilitate causal inference for routine applications. This article uses the sequential r...

Conventional multiple imputation (MI) (Rubin, 1987) replaces the missing values in a dataset by m > 1 sets of simulated values. We describe a two-stage extension of MI in which the missing values are partitioned into two groups and imputed N = mn times in a nested fashion. Two-stage MI divides the missing information into two components of variability, lending insight when the missing values ar...

Missing values, common in epidemiologic studies, are a major issue in obtaining valid estimates. Simulation studies have suggested that multiple imputation is an attractive method for imputing missing values, but it is relatively complex and requires specialized software. For each of 28 studies in the Asia Pacific Cohort Studies Collaboration, a comparison of eight imputation procedures (uncond...

Balancing the distributions of confounders across exposure levels in an observational study through matching or weighting is accepted method to control for confounding due these variables when estimating association between and outcome reduce degree dependence on certain modeling assumptions. Despite increasing popularity practice, procedures cannot be immediately applied datasets with missing ...