Karolina Gliszczyńska-Schroeder
arXiv 24 Sep 2026 · Econometrics
arXiv:2609.29440 · PDF · Extracted main text
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 effectiveness of estimators, especially those based on propensity scores, potentially leading to unreliable results. We investigate the effectiveness of kernel balancing (KBal) (Hazlett, 2020) as an alternative to propensity score methods for conditional average treatment effect (CATE) estimation, particularly in settings with overlap violations. Building on optimization-based balancing approaches, we integrate KBal weights into tree-based methods, specifically, causal forests (Athey et al., 2019) and the X-Learner (XRF) (Künzel et al., 2019), to assess their impact on bias reduction and estimation precision. Monte Carlo evidence shows that KBal achieves near-exact balance in a transformed feature space, thereby improving treatment effect estimation in cases where traditional reweighting methods struggle due to extreme weights, finite-sample bias, or insufficient removal of pre-existing confounding bias. We apply the proposed methods to the semi-synthetic IHDP benchmark dataset. Overall, the results indicate that KBal leads to performance improvements, especially in settings with nonlinear treatment effects and limited overlap, making it a useful alternative to propensity score methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cousineau, Martin and Verter, Vedat and Murphy, Susan A and Pineau,… (2023) Estimating causal effects with optimization-based methods: A review and empirical comparison | 1.000 | 7 | 4 | 100% |
| 2 | Jennifer L. Hill (2011) Bayesian Nonparametric Modeling for Causal Inference | 1.000 | 5 | 3 | 100% |
| 3 | Hazlett, Chad (2020) Kernel balancing | 0.941 | 12 | 6 | 83% |
| 4 | Susan Athey and Julie Tibshirani and Stefan Wager (2019) Generalized random forests | 0.941 | 6 | 4 | 83% |
| 5 | Wager, Stefan and Athey, Susan (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.941 | 6 | 4 | 83% |
| 6 | Künzel, Sören R and Sekhon, Jasjeet S and Bickel, Peter J and Yu, Bin (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.909 | 12 | 5 | 75% |
| 7 | Breiman, Leo (2001) Random Forests | 0.737 | 3 | 2 | 100% |
| 8 | Athey, Susan and Imbens, Guido (2016) Recursive partitioning for heterogeneous causal effects | 0.737 | 3 | 2 | 100% |
| 9 | Hainmueller, Jens (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 0.644 | 2 | 2 | 100% |
| 10 | Ramey, Craig T and Bryant, Donna M and Wasik, Barbara H and Sparling… (1992) Infant Health and Development Program for low birth weight, premature infants: Program elements, family participation, and child… | 0.644 | 2 | 2 | 100% |
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