Principal ignorability in mediation analysis: through and beyond sequential ignorability

被引:25
作者
Forastiere, Laura [1 ]
Mattei, Alessandra [1 ]
Ding, Peng [2 ]
机构
[1] Univ Florence, Dept Stat, Comp Sci, Applicat, Viale Morgagni 59, I-50134 Florence, Italy
[2] Univ Calif Berkeley, Dept Stat, 425 Evans Hall, Berkeley, CA 94720 USA
基金
美国国家科学基金会;
关键词
Causal inference; Identification; Potential outcome; Principal stratification; STRATIFICATION; INFERENCE;
D O I
10.1093/biomet/asy053
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as comparisons of observable potential outcomes. We establish a novel bridge between mediation analysis and principal stratification, which helps to clarify and weaken the commonly used identifying assumptions for natural direct and indirect effects. Using principal stratification, we show how sequential ignorability extrapolates from observable potential outcomes to a priori counterfactuals, and propose alternative weaker principal ignorability-type assumptions. We illustrate the key concepts using a clinical trial.
引用
收藏
页码:979 / 986
页数:8
相关论文
共 17 条
[11]   Identification, Inference and Sensitivity Analysis for Causal Mediation Effects [J].
Imai, Kosuke ;
Keele, Luke ;
Yamamoto, Teppei .
STATISTICAL SCIENCE, 2010, 25 (01) :51-71
[12]   On the use of propensity scores in principal causal effect estimation [J].
Jo, Booil ;
Stuart, Elizabeth A. .
STATISTICS IN MEDICINE, 2009, 28 (23) :2857-2875
[13]  
Pearl J., 2001, P 17 C UNCERTAINTY A, P411, DOI DOI 10.5555/2074022.2074073
[14]   IDENTIFIABILITY AND EXCHANGEABILITY FOR DIRECT AND INDIRECT EFFECTS [J].
ROBINS, JM ;
GREENLAND, S .
EPIDEMIOLOGY, 1992, 3 (02) :143-155
[15]   A review of causal estimation of effects in mediation analyses [J].
Ten Have, Thomas R. ;
Joffe, Marshall M. .
STATISTICAL METHODS IN MEDICAL RESEARCH, 2012, 21 (01) :77-107
[16]  
van der Laan MJ, 2008, INT J BIOSTAT, V4
[17]   Simple relations between principal stratification and direct and indirect effects [J].
VanderWeele, Tyler J. .
STATISTICS & PROBABILITY LETTERS, 2008, 78 (17) :2957-2962