Causal Fairness Analysis of ADHD Status and High School STEM Outcomes
Shuhan Ai

TL;DR
This paper applies a causal fairness framework to decompose ADHD's impact on high school STEM outcomes, revealing significant direct effects on GPA and racial disparities, with minimal impact on science identity.
Contribution
It introduces a causal decomposition approach to analyze ADHD-related disparities in STEM, highlighting the importance of direct effects and social identity interactions.
Findings
ADHD has a significant negative effect on STEM GPA (TV = -0.670).
Majority of the GPA disparity is due to direct effects not mediated by confounders.
Effect on science identity is small and statistically non-significant.
Abstract
This study applies the Causal Fairness Analysis (CFA) framework of Plecko and Bareinboim (2024) to decompose the total variation in STEM outcomes attributable to ADHD status into direct, indirect, and spurious components using Pearl's Structural Causal Model. Drawing on nationally representative data from the High School Longitudinal Study of 2009, this study examines two outcomes: cumulative STEM GPA and science identity. Total variation decomposition reveals a statistically significant ADHD penalty on STEM GPA (TV = -0.670), of which 63.3% is attributable to the direct effect (x-DE), indicating that the majority of the disparity operates through pathways not mediated by observed sociodemographic or academic confounders. In contrast, the effect on science identity is small and non-significant (TV = -0.068). Counterfactual direct effect analysis using the one-step debiased estimator…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
