Estimating Effects of Time-Varying Special Education Regimes Using Targeted Maximum Likelihood
Year: 2026
Type: preprint
Abstract: Longitudinal treatments are common in social science research but are often reduced to ever-versus-never exposure indicators that obscure their time-varying nature. This study shows how causal questions can be clarified by defining treatments through explicit intervention regimes rather than collapsed summaries. We introduce this framework by linking the longitudinal setting to single-time-point causal inference and extending it to time-varying exposures, sequential identification assumptions, and estimation using targeted maximum likelihood estimation (TMLE). We also address practical challenges, including missing data and positivity violations. Using data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K), we examine the effect of special education participation on 5th-grade mathematics achievement. Comparing static and dynamic regimes, we find meaningful differences: always-treated versus never-treated contrasts yield the largest negative estimates, while rule-based dynamic regimes produce substantially smaller effects. These results highlight the importance of well-defined interventions for interpreting longitudinal treatment effects.
Source: PsyArXiv (OSF Preprints)
Authors: Bryan Keller, Lindsey Liu, Yingqi Huan
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213511934