Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”

2003/12/01 by Junni L. Zhang, Donald B. Rubin · 4 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · doi:10.3102/10769986028004353

openalex publication_date 2003/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/09

Abstract

The topic of “truncation by death” in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither “censored” nor “missing,” but should be treated as being defined on an extended sample space. Using an educational example to illustrate, we will outline here a formulation for tackling this issue, where we call the outcome “truncated by death” because there is no hidden value of the outcome variable masked by the truncating event. We first formulate the principal stratification ( Frangakis & Rubin, 2002 ) approach, and we then derive large sample bounds for causal effects within the principal strata, with or without various identification assumptions. Extensions are then briefly discussed.

Citations

• On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
• Inference and missing data
• Estimating causal effects of treatments in randomized and nonrandomized studies.

Cited by

• Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes
• Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
• Quantifying and Detecting Individual Level `Always Survivor' Causal Effects Under `Truncation by Death' and Censoring Through Time
• Principal stratification [wikipedia]