The Evaluation of Causal Effects in Studies with an Unobserved Exposure/Outcome Variable: Bounds and Identification

TL;DR

This paper develops criteria and bounds for estimating causal effects from observational data when key exposure or outcome variables are unobserved, especially with multiple categories and unmeasured confounders.

Contribution

It introduces new identifiability criteria for multi-category unobserved variables and provides tightest bounds considering unmeasured confounders, advancing causal inference methods.

Findings

Identifiability criteria for multi-category unobserved variables

Tightest bounds for causal effects with unmeasured confounders

Applicable to practical scenarios with unobservable variables

Abstract

This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/ outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/ outcome variable in many practical fields.