# Recursive Partitioning for Personalization using Observational Data

arXiv: https://arxiv.org/abs/1608.08925 · HTML: https://mlsanity.com/paper/1608.08925/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 28 of 28)

- [Estimating Average Treatment Effects via Orthogonal Regularization](https://mlsanity.com/paper/2101.08490/) (2021)
- [The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations](https://mlsanity.com/paper/2209.01874/) (2022)
- [NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments](https://mlsanity.com/paper/2103.11175/) (2021)
- [Treatment Effect Risk: Bounds and Inference](https://mlsanity.com/paper/2201.05893/) (2022)
- [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)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [Constrained Prescriptive Trees via Column Generation](https://mlsanity.com/paper/2207.10163/) (2022)
- [Hierarchical and Density-based Causal Clustering](https://mlsanity.com/paper/2411.01250/) (2024)
- [Causal Effect Estimation using Variational Information Bottleneck](https://mlsanity.com/paper/2110.13705/) (2021)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities](https://mlsanity.com/paper/2112.08268/) (2021)
- [ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription](https://mlsanity.com/paper/2307.15691/) (2023)
- [CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression](https://mlsanity.com/paper/2310.16819/) (2023)
- [Learning Optimal Classification Trees Robust to Distribution Shifts](https://mlsanity.com/paper/2310.17772/) (2023)
- [Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression](https://mlsanity.com/paper/2202.05245/) (2022)
- [Learning Prescriptive ReLU Networks](https://mlsanity.com/paper/2306.00651/) (2023)
- [Optimal Policy Learning with Observational Data in Multi-Action Scenarios: Estimation, Risk Preference, and Potential Failures](https://mlsanity.com/paper/2403.20250/) (2024)
- [Robust and Heterogenous Odds Ratio: Estimating Price Sensitivity for Unbought Items](https://mlsanity.com/paper/2106.11389/) (2021)
- [Approximating Permutations with Neural Network Components for Travelling Photographer Problem](https://mlsanity.com/paper/2205.00242/) (2022)
- [Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation](https://mlsanity.com/paper/2212.06312/) (2022)
- [Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests](https://mlsanity.com/paper/2311.00577/) (2023)
- [PresAIse, A Prescriptive AI Solution for Enterprises](https://mlsanity.com/paper/2402.02006/) (2024)
- [Applications of 0-1 Neural Networks in Prescription and Prediction](https://mlsanity.com/paper/2402.18851/) (2024)
- [Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects](https://mlsanity.com/paper/2403.03240/) (2024)
- [Robust personalized pricing under uncertainty of purchase probabilities](https://mlsanity.com/paper/2407.15332/) (2024)
- [Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data](https://mlsanity.com/paper/2607.14318/) (2026)
- [Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence](https://mlsanity.com/paper/2609.16952/) (2026)

## Abstract (arXiv)

We study the problem of learning to choose from m discrete treatment options (e.g., news item or medical drug) the one with best causal effect for a particular instance (e.g., user or patient) where the training data consists of passive observations of covariates, treatment, and the outcome of the treatment. The standard approach to this problem is regress and compare: split the training data by treatment, fit a regression model in each split, and, for a new instance, predict all m outcomes and pick the best. By reformulating the problem as a single learning task rather than m separate ones, we propose a new approach based on recursively partitioning the data into regimes where different treatments are optimal. We extend this approach to an optimal partitioning approach that finds a globally optimal partition, achieving a compact, interpretable, and impactful personalization model. We develop new tools for validating and evaluating personalization models on observational data and use these to demonstrate the power of our novel approaches in a personalized medicine and a job training application.