# Estimation of Treatment Effects in Extreme and Unobserved Data

arXiv: https://arxiv.org/abs/2506.14051 · HTML: https://mlsanity.com/paper/2506.14051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Orthogonal Statistical Learning](https://mlsanity.com/paper/1901.09036/) (2019)
- [Double/Debiased Machine Learning for Treatment and Causal Parameters](https://mlsanity.com/paper/1608.00060/) (2016)
- [Causal discovery in heavy-tailed models](https://mlsanity.com/paper/1908.05097/) (2019)
- [Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions](https://mlsanity.com/paper/2110.06627/) (2021)
- [When is the estimated propensity score better? High-dimensional analysis and bias correction](https://mlsanity.com/paper/2303.17102/) (2023)
- [Kernel PCA for multivariate extremes](https://mlsanity.com/paper/2211.13172/) (2022)
- [Treatment Effects in Extreme Regimes](https://mlsanity.com/paper/2306.11697/) (2023)
- [Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes](https://mlsanity.com/paper/2312.09862/) (2023)

## Abstract (arXiv)

Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. But what if we are interested in estimating the effects of a policy intervention whose benefits, while potentially important, can only be observed and measured in rare yet impactful events, such as extreme climate events? The standard causal inference methodology is not designed for this type of inference since the events of interest may be scarce in the observed data and some degree of extrapolation is necessary. Extreme Value Theory (EVT) provides methodologies for analyzing statistical phenomena in such extreme regimes. We introduce a novel framework for assessing treatment effects in extreme data to capture the causal effect at the occurrence of rare events of interest. In particular, we employ the theory of multivariate regular variation to model extremities. We develop a consistent estimator for extreme treatment effects and present a rigorous non-asymptotic analysis of its performance. We illustrate the performance of our estimator using both synthetic and semi-synthetic data.