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Some methods for heterogeneous treatment effect estimation in high-dimensions

arXiv 1707.00102 ↗First posted 1 Jul 2017stat.MLcited by 48

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Abstract (arXiv)

When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials. Stanford Health Care alone has millions of electronic medical records (EMRs) that are only just recently being leveraged to inform better treatment recommendations. These data present a unique challenge because they are high-dimensional and observational. Our goal is to make personalized treatment recommendations based on the outcomes for past patients similar to a new patient. We propose and analyze three methods for estimating heterogeneous treatment effects using observational data. Our methods perform well in simulations using a wide variety of treatment effect functions, and we present results of applying the two most promising methods to data from The SPRINT Data Analysis Challenge, from a large randomized trial of a treatment for high blood pressure.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2017.

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Citing papers per year: 2021: 10; 2022: 12; 2023: 12; 2024: 5; 2025: 8; 2026: 1.

48 papers.

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  1. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  2. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  3. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  4. 2021Learning Optimal Prescriptive Trees from Observational Datacited by 10
  5. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
  6. 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
  7. 2022Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomescited by 6
  8. 2023Leveraging text data for causal inference using electronic health recordscited by 5
  9. 2021Federated Estimation of Causal Effects from Observational Datacited by 4
  10. 2021Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Datacited by 4
  11. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  12. 2022Ensemble Method for Estimating Individualized Treatment Effectscited by 4
36 more
  1. 2022What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?cited by 4
  2. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  3. 2023Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effectscited by 4
  4. 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
  5. 2022Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forestscited by 3
  6. 2023The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effectscited by 3
  7. 2023When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspectivecited by 3
  8. 2024Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learningcited by 3
  9. 2021To do or not to do: cost-sensitive causal decision-makingcited by 2
  10. 2021Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunitiescited by 2
  11. 2022Heterogeneous Synthetic Learner for Panel Datacited by 2
  12. 2023How to select predictive models for causal inference?cited by 2
  13. 2023CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regressioncited by 2
  14. 2024Federated Learning for Estimating Heterogeneous Treatment Effectscited by 2
  15. 2022Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regressioncited by 1
  16. 2022A Double Machine Learning Trend Model for Citizen Science Datacited by 1
  17. 2022Robust Direct Learning for Causal Data Fusioncited by 1
  18. 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1
  19. 2023Data-Driven Estimation of Heterogeneous Treatment Effectscited by 1
  20. 2023Learning Prescriptive ReLU Networkscited by 1
  21. 2024Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effectscited by 1
  22. 2025Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modelingcited by 1
  23. 2025Horseshoe Forests for High-Dimensional Causal Survival Analysiscited by 1
  24. 2025A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimatorscited by 1
  25. 2021A Twin Neural Model for Upliftcited by 0
  26. 2021Adaptive Multi-Source Causal Inferencecited by 0
  27. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  28. 2023Generalized Random Forests using Fixed-Point Treescited by 0
  29. 2024Applications of 0-1 Neural Networks in Prescription and Predictioncited by 0
  30. 2024Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effectscited by 0
  31. 2025Causal rule ensemble approach for multi-arm datacited by 0
  32. 2025Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Predictioncited by 0
  33. 2025Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growthcited by 0
  34. 2025Deep Learning of Continuous and Structured Policies for Aggregated Heterogeneous Treatment Effectscited by 0
  35. 2025Assessment of the conditional exchangeability assumption in causal machine learning models: a simulation studycited by 0
  36. 2026Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settingscited by 0