Add to ORKGOne 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
Background and objectivesHeat related mortality is of great concern for public health, and estimates...
This paper presents the findings of climate change impact on a widespread human crisis due to a natu...
Machine learning (ML) and in particular deep learning (DL) methods push state-of-the-art solutions f...
Several important questions cannot be answered with the standard toolkit of causal inference since a...
Climate change increasingly affects every aspect of human life. Recent studies report a close correl...
A recent paper utilized a deep learning methodology when analyzing multivariate time series data to ...
Summarization: Understanding and estimating regional climate change under different anthropogenic em...
Machine learning methods have recently created high expectations in the climate modelling context in...
Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperature...
Estimation of future mortality rates still plays a central role among life insurers in pricing their...
A general issue in climate science is the handling of big data and running complex and computational...
Machine learning is becoming an increasingly important tool for climate scientists, but hampered by ...
This paper shows that skillful week 3–4 predictions of a large-scale pattern of 2 m temperature over...
Many research questions in Earth and environmental sciences are inherently causal, requiring robus...
Time-series profiles derived from temperature proxies such as tree rings can provide information abo...
Background and objectivesHeat related mortality is of great concern for public health, and estimates...
This paper presents the findings of climate change impact on a widespread human crisis due to a natu...
Machine learning (ML) and in particular deep learning (DL) methods push state-of-the-art solutions f...
Several important questions cannot be answered with the standard toolkit of causal inference since a...
Climate change increasingly affects every aspect of human life. Recent studies report a close correl...
A recent paper utilized a deep learning methodology when analyzing multivariate time series data to ...
Summarization: Understanding and estimating regional climate change under different anthropogenic em...
Machine learning methods have recently created high expectations in the climate modelling context in...
Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperature...
Estimation of future mortality rates still plays a central role among life insurers in pricing their...
A general issue in climate science is the handling of big data and running complex and computational...
Machine learning is becoming an increasingly important tool for climate scientists, but hampered by ...
This paper shows that skillful week 3–4 predictions of a large-scale pattern of 2 m temperature over...
Many research questions in Earth and environmental sciences are inherently causal, requiring robus...
Time-series profiles derived from temperature proxies such as tree rings can provide information abo...
Background and objectivesHeat related mortality is of great concern for public health, and estimates...
This paper presents the findings of climate change impact on a widespread human crisis due to a natu...
Machine learning (ML) and in particular deep learning (DL) methods push state-of-the-art solutions f...