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dc.contributor.authorHovdahl, Isabel
dc.date.accessioned2019-06-26T09:28:34Z
dc.date.available2019-06-26T09:28:34Z
dc.date.issued2019-06
dc.identifier.issn1892-2198
dc.identifier.urihttp://hdl.handle.net/11250/2602267
dc.description.abstractOne 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.nb_NO
dc.language.isoengnb_NO
dc.publisherBI Norwegian Business Schoolnb_NO
dc.relation.ispartofseriesCAMP Working Paper Series Paper;05/2019
dc.subjectClimate changenb_NO
dc.subjectmachine learningnb_NO
dc.subjectmortalitynb_NO
dc.titleOn the use of machine learning for causal inference in climate economicsnb_NO
dc.typeWorking papernb_NO
dc.source.pagenumber51nb_NO


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