Flexible Models for the Estimation of Treatment Effect
Estimation of treatment effect is an important problem which is well studied in the literature. While the regression models are one of the most commonly used techniques for the estimation of treatment effect, they are prone to model misspecification. To minimize the model misspecification bias, flexible nonparametric models are introduced for the estimation. Continuing this line of research, we propose two flexible nonparametric models that allow the treatment effect to vary across different levels of covariates. We provide estimation algorithms for both these models. Using simulations and data analysis, we illustrate the usefulness of the proposed methods.
Bashir, Habeeb Abolaji, "Flexible Models for the Estimation of Treatment Effect" (2023). ETD Collection for University of Texas, El Paso. AAI30493744.