The paper focuses on establishing causation in regression analysis in observational settings. Simple static regression analysis cannot establish causality in the absence of a priori theory on possible causal mechanisms or controlled and randomized experiments. However, two regression based econometric techniques – instrumental variables and Granger causality - can be used to test for causality given some assumptions. The Granger causality technique is applied to a time series data set on energy and economic growth from Sweden spanning 150 years to determine whether increases in energy use and energy quality have driven economic growth. I show that the Granger causality technique is very sensitive to variable definition, choice of additional variables in the model, and sample periods. Better results can be obtained by using multivariate models, defining variables to better reflect their theoretical definition, and by using larger samples. The better specified models with larger samples are more likely to show that energy causes output growth but it is also possible that the relationship between energy and growth has changed over time. Energy prices have a significant causal impact on both energy use and output while there is no strong evidence that energy use causes carbon and sulfur emissions despite the obvious physical relationship.