# Treatment Effects in Extreme Regimes

arXiv: https://arxiv.org/abs/2306.11697 · HTML: https://mlsanity.com/paper/2306.11697/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Semiparametric counterfactual density estimation](https://mlsanity.com/paper/2102.12034/) (2021)
- [Treatment Effect Risk: Bounds and Inference](https://mlsanity.com/paper/2201.05893/) (2022)
- [Efficient Estimation of Quantiles in Missing Data Models](https://mlsanity.com/paper/1512.08110/) (2015)
- [Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores](https://mlsanity.com/paper/2102.01753/) (2021)

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

- [Estimation of Treatment Effects in Extreme and Unobserved Data](https://mlsanity.com/paper/2506.14051/) (2025)

## Abstract (arXiv)

Understanding treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the unavailability of counterfactual outcomes and the rarity and difficulty of collecting extreme data in practice. To address this issue, we propose a new framework based on extreme value theory for estimating treatment effects in extreme regimes. We quantify these effects using variations in tail decay rates of potential outcomes in the presence and absence of treatments. We establish algorithms for calculating these quantities and develop related theoretical results. We demonstrate the efficacy of our approach on various standard synthetic and semi-synthetic datasets.