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Karolina Gliszczyńska-Schroeder

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Preprint Sep 2026

Estimating Heterogeneous Causal Effects with Tree-Based Methods under Imperfect Compliance

We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method,...

Karolina Gliszczyńska-Schroeder · 0 citations

Preprint Sep 2026

Kernel Balancing in Tree-based Methods

Studying heterogeneous treatment effects has become essential in experimental and observational studies. A critical assumption for obtaining reliable treatment effect estimates is overlap, which requires that treated and control units have sufficiently similar covariate distributions. Poor overlap may limit the effecti...

Karolina Gliszczyńska-Schroeder · 0 citations

Preprint Sep 2026

Median-based Splitting Rules for Causal Trees and Forests

Heavy-tailed and skewed outcomes are common in the randomized experiments and observational studies used to estimate heterogeneous treatment effects, yet the mean-squared-error criterion that guides splitting in honest causal trees is sensitive to the extreme values they generate. Building on the causal forest framewor...

Lennard Maßmann, Karolina Gliszczyńska-Schroeder · 0 citations