Year: 2025
Type: article
Abstract: Bounding causal effects under unmeasured confounding is particularly challenging when the outcome variable is ordinal. When the goal is to assess whether an intervention leads to a better outcome, ordinal causal effects offer a more appropriate analytical framework. In contrast, the average causal effect (ACE), defined as the difference in expected outcomes, is more suitable for capturing population-level effects. In this paper, we derive sharp bounds for causal effects with ternary outcomes using an expectation-based framework, under both general conditions and monotonicity assumptions. We conduct numerical simulations to evaluate the width of the bounds under various scenarios. Finally, we demonstrate our method’s practical utility by applying it to the CDC Diabetes Health Indicators Dataset to assess the causal effect of health behaviors on diabetes risk.
Source: Mathematics
DOI: 10.3390/math13193103
Authors: Ailei Ding, Hanmei Sun
Institution: Shandong Normal University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1336862203, G4493651740
OpenAlex ID: W4414591665