# Machine learning reveals how personalized climate communication can both succeed and backfire

arXiv: https://arxiv.org/abs/2109.05104 · HTML: https://mlsanity.com/paper/2109.05104/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [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)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Uplift Modeling with Multiple Treatments and General Response Types](https://mlsanity.com/paper/1705.08492/) (2017)
- [Uplift Modeling for Multiple Treatments with Cost Optimization](https://mlsanity.com/paper/1908.05372/) (2019)

## Built on by (most cited first, 3 of 3)

- [On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments](https://mlsanity.com/paper/2407.08067/) (2024)
- [Understanding and Shifting Preferences for Battery Electric Vehicles](https://mlsanity.com/paper/2202.08963/) (2022)
- [Leveraging Language Models and Bandit Algorithms to Drive Adoption of Battery-Electric Vehicles](https://mlsanity.com/paper/2410.23371/) (2024)

## Abstract (arXiv)

Different advertising messages work for different people. Machine learning can be an effective way to personalise climate communications. In this paper we use machine learning to reanalyse findings from a recent study, showing that online advertisements increased some people's belief in climate change while resulting in decreased belief in others. In particular, we show that the effect of the advertisements could change depending on people's age and ethnicity.