# Some methods for heterogeneous treatment effect estimation in high-dimensions

arXiv: https://arxiv.org/abs/1707.00102 · HTML: https://mlsanity.com/paper/1707.00102/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2017.

## Built on by (most cited first, 30 of 48)

- [Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression](https://mlsanity.com/paper/2102.08208/) (2021)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects](https://mlsanity.com/paper/2301.00346/) (2023)
- [Learning Optimal Prescriptive Trees from Observational Data](https://mlsanity.com/paper/2108.13628/) (2021)
- [Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects](https://mlsanity.com/paper/2205.14714/) (2022)
- [Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation](https://mlsanity.com/paper/2102.06573/) (2021)
- [Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes](https://mlsanity.com/paper/2206.04907/) (2022)
- [Leveraging text data for causal inference using electronic health records](https://mlsanity.com/paper/2307.03687/) (2023)
- [Federated Estimation of Causal Effects from Observational Data](https://mlsanity.com/paper/2106.00456/) (2021)
- [Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data](https://mlsanity.com/paper/2112.07602/) (2021)
- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Ensemble Method for Estimating Individualized Treatment Effects](https://mlsanity.com/paper/2202.12445/) (2022)
- [What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?](https://mlsanity.com/paper/2206.10323/) (2022)
- [Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression](https://mlsanity.com/paper/2207.09139/) (2022)
- [Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects](https://mlsanity.com/paper/2303.16299/) (2023)
- [Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning](https://mlsanity.com/paper/2306.01086/) (2023)
- [When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective](https://mlsanity.com/paper/2311.14110/) (2023)
- [Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests](https://mlsanity.com/paper/2210.02836/) (2022)
- [The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/2307.09700/) (2023)
- [Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning](https://mlsanity.com/paper/2407.19078/) (2024)
- [To do or not to do: cost-sensitive causal decision-making](https://mlsanity.com/paper/2101.01407/) (2021)
- [Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities](https://mlsanity.com/paper/2112.08268/) (2021)
- [Heterogeneous Synthetic Learner for Panel Data](https://mlsanity.com/paper/2212.14580/) (2022)
- [How to select predictive models for causal inference?](https://mlsanity.com/paper/2302.00370/) (2023)
- [CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression](https://mlsanity.com/paper/2310.16819/) (2023)
- [Federated Learning for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2402.17705/) (2024)
- [Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression](https://mlsanity.com/paper/2202.05245/) (2022)
- [A Double Machine Learning Trend Model for Citizen Science Data](https://mlsanity.com/paper/2210.15524/) (2022)
- [Robust Direct Learning for Causal Data Fusion](https://mlsanity.com/paper/2211.00249/) (2022)
- [BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect](https://mlsanity.com/paper/2211.10793/) (2022)

## 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.