Lennard Maßmann, Jens Klenke
arXiv 16 Sep 2026 · Econometrics
arXiv:2609.18903 · PDF · Extracted main text
Discovering interpretable subgroups whose complier effects deviate from the average is a central goal of instrumental variable analysis under imperfect compliance, yet existing tree-based methods degrade when most covariates are irrelevant to the effect. We propose Shrinkage Bayesian Causal Forest with Instrumental Variable (SBCF-IV) for discovering and estimating subgroups with heterogeneous Complier Average Causal Effects (CACE) in sparse high-dimensional settings. SBCF-IV places a sparsity-inducing Dirichlet prior on the splitting probabilities of the Bayesian Additive Regression Trees that estimate the conditional intention-to-treat and the complier share, concentrating posterior mass on the few covariates that moderate the complier effect and thereby regularizing effect estimation. The posterior split frequencies additionally enter a downstream CART as variable-level costs that steer the partition toward relevant moderators, providing an interpretable division of the covariate space. Monte Carlo experiments show that, as the share of irrelevant covariates grows, SBCF-IV recovers the true partition more reliably than its non-sparse predecessor BCF-IV at the tree and unit level, and retains nominal coverage where BCF-IV's intervals deteriorate. We apply the method to the Oregon Health Insurance Experiment and the 401(k) eligibility data.
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| Reference | Intensity | Mentions | Sections | Main text | |
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| 1 | Johnson, Michael and Cao, Jiongyi and Kang, Hyunseung (2022) Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon health insurance experiment | 0.863 | 14 | 3 | 64% |
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| 3 | Bargagli-Stoffi, Falco J. and Witte, Kristof De and Gnecco, Giorgio (2022) Heterogeneous causal effects with imperfect compliance: A Bayesian machine learning approach | 0.843 | 20 | 9 | 60% |
| 4 | Angrist, Joshua D. and Imbens, Guido W. and Rubin, Donald B (1996) Identification of Causal Effects Using Instrumental Variables | 0.843 | 5 | 4 | 60% |
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| 9 | Hill, Jennifer L (2011) Bayesian Nonparametric Modeling for Causal Inference | 0.737 | 5 | 4 | 40% |
| 10 | Breiman, Leo and Friedman, Jerome and Olshen, R. A. and Stone, Charl… (1984) Classification and Regression Trees | 0.737 | 3 | 3 | 67% |
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