Estimating returns to special education: combining machine learning and text analysis to address confounding

Abstract

While the number of students with identified special needs is increasing in developed countries, there is little evidence on academic outcomes and labor market integration returns to special education. I present results from the first ever study to examine short-and long-term returns to special education programs using recent methods in causal machine learning and computational text analysis. I find that special education programs in inclusive settings have positive returns on academic performance in math and language as well as on employment and wages. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. However, I find that segregation has benefits for some students: students with emotional or behavioral problems, and nonnative students. Finally, using shallow decision trees, I deliver optimal placement rules that increase overall returns for students with special needs and lower special education costs. These placement rules would reallocate most students with special needs from segregation to inclusion, which reinforces the conclusion that inclusion is beneficial to students with special needs.

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

“…In this paper, we focus on the above-listed meta-learners in order to provide a contrast between less and more complex algorithms for the estimation of causal effects. Moreover, these particular meta-learners have been extensively studied theoretically as well as applied in various empirical settings, including economics (Knaus, 2020;Jacob, 2021;Sallin, 2021;Valente, 2022), public policy (Kristjanpoller, Michell, & Minutolo, 2021;Shah, Kreif, & Jones, 2021), marketing (Gubela, Lessmann, & Jaroszewicz, 2020;Gubela & Lessmann, 2021), medicine (Lu, Sadiq, Feaster, & Ishwaran, 2018;Duan, Rajpurkar, Laird, Ng, & Basu, 2019) or sports (Goller, 2021). Some further examples of meta-learners proposed in the literature consist of the U-learner and Y-learner (Stadie, Kunzel, Vemuri, & Sekhon, 2018), or more recently the IF-learner (Curth, Alaa, & van der Schaar, 2020) and RA-learner (Curth & van der Schaar, 2021), which are, however, beyond the scope of this paper.…”

Essays on causal inference

“…They apply this framework to control for and match based on the content of publications in order to estimate how a scholar's gender affects the number of citations of his/her publications. Other studies that rely on text-based confounding adjustment include those bySallin (2021) andVeitch, Sridhar, and Blei (2020) Mozer, Miratrix, Kaufman, and Anastasopoulos (2020). andField, Park, and Tsvetkov (2020) propose a text matching approach based on distance metrics rather than text classification.…”