Identification of causal direct-indirect effects without untestable assumptions
Methodology
2020-09-29 v1 Statistics Theory
Statistics Theory
Abstract
In causal mediation analysis, identification of existing causal direct or indirect effects requires untestable assumptions in which potential outcomes and potential mediators are independent. This paper defines a new causal direct and indirect effect that does not require the untestable assumptions. We show that the proposed measure is identifiable from the observed data, even if potential outcomes and potential mediators are dependent, while the existing natural direct or indirect effects may find a pseudo-indirect effect when the untestable assumptions are violated.
Keywords
Cite
@article{arxiv.2009.12528,
title = {Identification of causal direct-indirect effects without untestable assumptions},
author = {Takahiro Hoshino},
journal= {arXiv preprint arXiv:2009.12528},
year = {2020}
}
Comments
The paper has been submitted and the discussion paper will appear in SSRN