# Uplift Modeling from Separate Labels

arXiv: https://arxiv.org/abs/1803.05112 · HTML: https://mlsanity.com/paper/1803.05112/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [A Functional Model Method for Nonconvex Nonsmooth Conditional Stochastic Optimization](https://mlsanity.com/paper/2405.10815/) (2024)
- [Estimation of Local Average Treatment Effect by Data Combination](https://mlsanity.com/paper/2109.05175/) (2021)
- [Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences](https://mlsanity.com/paper/2107.08135/) (2021)
- [Non parametric estimation of causal populations in a counterfactual scenario](https://mlsanity.com/paper/2112.04288/) (2021)
- [Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions](https://mlsanity.com/paper/2212.13145/) (2022)
- [Multiple Instance Learning for Uplift Modeling](https://mlsanity.com/paper/2312.09639/) (2023)

## Abstract (arXiv)

Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be jointly equipped with two types of labels: the taken action and its outcome. However, obtaining two labels for each instance at the same time is difficult or expensive in many real-world problems. In this paper, we propose a novel method of uplift modeling that is applicable to a more practical setting where only one type of labels is available for each instance. We show a mean squared error bound for the proposed estimator and demonstrate its effectiveness through experiments.