# Preference-based Conditional Treatment Effects and Policy Learning

arXiv: https://arxiv.org/abs/2602.03823 · HTML: https://mlsanity.com/paper/2602.03823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 62 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Probabilities of Causation: Bounds and Identification](https://mlsanity.com/paper/1301.3898/) (2013)
- [Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach](https://mlsanity.com/paper/2111.12161/) (2021)
- [Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression](https://mlsanity.com/paper/2102.08208/) (2021)
- [Personalized Decision Making -- A Conceptual Introduction](https://mlsanity.com/paper/2208.09558/) (2022)
- [Probabilities of Causation with Nonbinary Treatment and Effect](https://mlsanity.com/paper/2208.09568/) (2022)
- [Robust and Agnostic Learning of Conditional Distributional Treatment Effects](https://mlsanity.com/paper/2205.11486/) (2022)
- [Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?](https://mlsanity.com/paper/2303.16008/) (2023)
- [Probabilities of Causation for Continuous and Vector Variables](https://mlsanity.com/paper/2405.20487/) (2024)
- [Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study](https://mlsanity.com/paper/2204.10969/) (2022)
- [Policy Learning with Distributional Welfare](https://mlsanity.com/paper/2311.15878/) (2023)
- [Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest](https://mlsanity.com/paper/2411.08778/) (2024)
- [Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds](https://mlsanity.com/paper/1507.01542/) (2015)
- [Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details](https://mlsanity.com/paper/2011.13058/) (2020)
- [Causal effect on a target population: a sensitivity analysis to handle missing covariates](https://mlsanity.com/paper/2105.06435/) (2021)
- [Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis](https://mlsanity.com/paper/2501.16933/) (2025)
- [Estimating Probabilities of Causation with Machine Learning Models](https://mlsanity.com/paper/2502.08858/) (2025)
- [The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference](https://mlsanity.com/paper/2502.10049/) (2025)
- [Optimal treatment regimes for the net benefit of a treatment](https://mlsanity.com/paper/2503.22580/) (2025)
- [Learning Probabilities of Causation with Mask-Augmented Data](https://mlsanity.com/paper/2505.17133/) (2025)

## Built on by (most cited first, 1 of 1)

- [Targeted Active Learning for Preference-Based Treatment Effects on Multivariate Outcomes](https://mlsanity.com/paper/2610.03824/) (2026)

## Abstract (arXiv)

We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CPTE requires only that outcomes be ranked under a preference rule, unlocking flexible modeling of heterogeneous effects with multivariate, ordinal, or preference-driven outcomes. This unifies applications such as conditional probability of necessity and sufficiency, conditional Win Ratio, and Generalized Pairwise Comparisons. Despite the intrinsic non-identifiability of comparison-based estimands, CPTE provides interpretable targets and delivers new identifiability conditions for previous unidentifiable estimands. We present estimation strategies via matching, quantile, and distributional regression, and further design efficient influence-function estimators to correct plug-in bias and maximize policy value. Synthetic and semi-synthetic experiments demonstrate clear performance gains and practical impact.