mlsanityData to 14 Oct 2026

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

arXiv 2109.05104 ↗First posted 10 Sep 2021cs.LG cs.CYcited by 3

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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.

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

5 of 24 references.

References · 5

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  3. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  4. 2017Uplift Modeling with Multiple Treatments and General Response Typescited by 41
  5. 2019Uplift Modeling for Multiple Treatments with Cost Optimizationcited by 25

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Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.