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

Totte Harinen, Alexandre Filipowicz, Shabnam Hakimi, Rumen Iliev, Matthew Klenk, Emily Sarah Sumner

Keywords: causal machine learning, treatment effect heterogeneity, climate change

TL;DR: We use causal machine learning to show large heterogeneity in how people responds to climate change ads

Abstract: 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 climate change belief in some people while resulting in decreased belief in others. In particular, we show that the effect of the advertisements could change depending on a person's age and ethnicity. Our findings have broad methodological and practical applications.