On the use of machine learning for causal inference in climate economics
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One of the most important research questions in climate economics is the relationship between temperatures and human mortality. This paper develops a procedure that enables the use of machine learning for estimating the causal temperature-mortality relationship. The machine-learning model is compared to a traditional OLS model, and although both models are capturing the causal temperature-mortality relationship, they deliver very diﬀerent predictions of the eﬀect of climate change on mortality. These diﬀerences are mainly caused by diﬀerent abilities regarding extrapolation and estimation of marginal eﬀects. The procedure developed in this paper can ﬁnd applications in other ﬁelds far beyond climate economics.