Regularization and confounding in linear regression for treatment effect estimation
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Abstract (arXiv)
This paper investigates the use of regularization priors in the context of treatment effect estimation using observational data where the number of control variables is large relative to the number of observations. First, the phenomenon of regularization-induced confounding is introduced, which refers to the tendency of regularization priors to adversely bias treatment effect estimates by over-shrinking control variable regression coefficients. Then, a simultaneous regression model is presented which permits regularization priors to be specified in a way that avoids this unintentional re-confounding. The new model is illustrated on synthetic and empirical data.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2016.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 6; 2026: 1.
12 papers.
- 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
- 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 12
- 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
- 2025Hybrid Meta-learners for Estimating Heterogeneous Treatment Effectscited by 4
- 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settingscited by 2
- 2025CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networkscited by 2
- 2022Interpretable Deep Causal Learning for Moderation Effectscited by 1
- 2024Ablation Studies for Novel Treatment Effect Estimation Modelscited by 1
- 2025A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designscited by 1
- 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
- 2025Generative Learner for Distributional Causal Effectscited by 0
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
- 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments6 Jul 2026
- 2025CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks26 Sep 2025
- 2025Horseshoe Forests for High-Dimensional Causal Survival Analysis29 Jul 2025
- 2025Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects16 Jun 2025
- 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiD14 May 2025
- 2025Generative Learner for Distributional Causal Effects23 Mar 2025
- 2025A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designs1 Mar 2025
- 2024Ablation Studies for Novel Treatment Effect Estimation Models21 Oct 2024
- 2023FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings28 Nov 2023
- 2022Interpretable Deep Causal Learning for Moderation Effects21 Jun 2022
- 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability16 Jun 2022
- 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation12 Feb 2021