On the use of machine learning for causal inference in climate economics
Isabel Hovdahl ()
No No 05/2019, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School
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.
Keywords: Climate change; machine learning; mortality (search for similar items in EconPapers)
Pages: 51 pages
New Economics Papers: this item is included in nep-big, nep-ecm, nep-env, nep-pay and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0077
Access Statistics for this paper
More papers in Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School Contact information at EDIRC.
Bibliographic data for series maintained by Helene Olsen ().