Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…

Background/Context: Observational studies often employ regression discontinuity (RD) designs and multiple control-group designs to explore the causal quantities of interest. RD designs assess policy and program effectiveness by assigning subjects to treatment based on whether they exceed a pre-defined cutoff. RD designs are classified into two…

Background/Context: Some observational studies involve multiple layers of treatment selection, specifically in the context of the extended time accommodation (ETA) for English language learners (ELLs). In ETA settings, the first selection occurs due to the eligibility rule, where students whose ELL English proficiency is below a certain threshold…

Background/Context: Regression discontinuity (RD) designs are used for policy and program evaluation where subjects' eligibility into a program or policy is determined by whether an assignment variable (i.e., running variable) exceeds a pre-defined cutoff. Under a standard RD design with a continuous assignment variable, the average treatment…