Identifiability of causal effects on a binary outcome within principal strata

doi:10.1007/s11464-011-0127-8

Front Math Chin

2011, Vol. 6

Issue (6) : 1249-1263 https://doi.org/10.1007/s11464-011-0127-8

RESEARCH ARTICLE

Identifiability of causal effects on a binary outcome within principal strata

Wei YAN¹, Peng DING¹, Zhi GENG¹(

), Xiaohua ZHOU^2,3,4

1. School of Mathematical Sciences, Peking University, Beijing 100871, China; 2. Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China; 3. Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; 4. Biostatistics Unit, HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101, USA

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Abstract

Principal strata are defined by the potential values of a posttreatment variable, and a principal effect is a causal effect within a principal stratum. Identifying the principal effect within every principal stratum is quite challenging. In this paper, we propose an approach for identifying principal effects on a binary outcome via a pre-treatment covariate. We prove the identifiability with single post-treatment intervention under the monotonicity assumption. Furthermore, we discuss the local identifiability with multicomponent intervention. Simulations are performed to evaluate our approach. We also apply it to a real data set from the Improving Mood-Promoting Access to Collaborate Treatment program.

Keywords Causal inference identifiability principal effect multi-component intervention

Corresponding Author(s): GENG Zhi,Email:zgeng@math.pku.edu.cn

Issue Date: 01 December 2011

Cite this article:

Wei YAN,Peng DING,Zhi GENG, et al. Identifiability of causal effects on a binary outcome within principal strata[J]. Front Math Chin, 2011, 6(6): 1249-1263.

URL:

https://academic.hep.com.cn/fmc/EN/10.1007/s11464-011-0127-8
https://academic.hep.com.cn/fmc/EN/Y2011/V6/I6/1249

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[2]	Na SHAN, Jianhua GUO. Covariate selection for identifying the effects of a particular type of conditional plan using causal networks[J]. Front Math Chin, 2010, 5(4): 687-700.

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