Karolina Gliszczyńska-Schroeder
arXiv 24 Sep 2026 · Statistics — Methodology
arXiv:2609.31769 · PDF · Extracted main text
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, we introduce a two-step, model-agnostic approach that allows any suitable machine learning method to be used for the CCACE estimation in the first step. Specifically, we implement two non-Bayesian tree-based methods, both using forest-based learners: DRRF-IV, a debiased transformed-outcome regression-forest approach, and a GRF-based IV adaptation of the generalized random forest framework (Athey et al., 2019). Through a simulation study, we assess the precision, bias, and the ability to correctly identify the underlying subgroup structure of the proposed methods relative to BCF-IV. The results show that the non-Bayesian methods perform competitively across the considered simulation settings, with performance improving for larger sample sizes and moderately large treatment effects, while reducing computational runtime. We apply our new methods by revisiting an empirical study that examines the effect of prompt admission to intensive care units (ICU) on 28-day mortality across 48 UK National Health Service hospitals. While previous work finds no significant overall treatment effect, we investigate whether subgroups of patients may benefit more from prompt ICU admission.
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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 | Bargagli-Stoffi, Falco J and De Witte, Kristof and Gnecco, Giorgio (2022) Heterogeneous causal effects with imperfect compliance: A Bayesian machine learning approach | 0.979 | 16 | 5 | 94% |
| 2 | Stoffi, Falco Joannes Bargagli and Gnecco, Giorgio (2018) Estimating heterogeneous causal effects in the presence of irregular assignment mechanisms | 0.941 | 6 | 3 | 83% |
| 3 | Luke Keele and Steve Harris and Samuel D. Pimentel and Richard Grieve (2020) Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensi… | 0.909 | 8 | 4 | 75% |
| 4 | Susan Athey and Julie Tibshirani and Stefan Wager (2019) Generalized random forests | 0.874 | 6 | 2 | 100% |
| 5 | Komura, Toshiaki and Bargagli Stoffi, Falco and Shiba, Koichiro and… (2025) Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability | 0.843 | 5 | 3 | 60% |
| 6 | Hahn, P Richard and Murray, Jared S and Carvalho, Carlos M (2020) Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion) | 0.843 | 3 | 3 | 100% |
| 7 | Athey, Susan and Imbens, Guido (2016) Recursive partitioning for heterogeneous causal effects | 0.737 | 3 | 2 | 100% |
| 8 | Joshua D. Angrist and Guido W. Imbens and Donald B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.644 | 2 | 2 | 100% |
| 9 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 10 | Künzel, Sören R and Sekhon, Jasjeet S and Bickel, Peter J and Yu, Bin (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.644 | 2 | 2 | 100% |
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