We conclude that the frequent practice of inferring geomorphic causation from well-performing models without accounting for data limitations is invalid. The impact-oriented intervention index, however, enabled to identify damage-causing landslides with high accuracy. where we find correlation we cannot always predict causation. Despite partly high predictive performances, the models were unable to create geomorphically plausible spatial predictions. Pearson himself does not use the phrase 'correlation does not imply causation' but is grappling with the relation between the two in The Grammar of Science, for example: All causation as we have defined it is correlation, but the converse is not necessarily true, i.e. The results revealed that none of the models reflected landslide susceptibility. The results were evaluated in terms of statistical relationships, variable importance, predictive performance, and geomorphic plausibility. Under nearly all circumstances, you cant say that your survey results cause, lead to, prove, or (insert. An association or correlation between variables simply indicates that the values. Always remember correlation does not imply causation.
#Causality does not imply correlation registration#
The study offers a novel perspective on how biases in landslide data can be considered within data-driven models by focusing not only on the process under investigation (landsliding), but also on the circumstances that led to the registration of landslide information (data collection effects). As youve no doubt heard, correlation doesnt necessarily imply causation. Action A relates to Action Bbut one event doesn’t necessarily cause the other event to happen.
![causality does not imply correlation causality does not imply correlation](https://e-abm.com/wp-content/uploads/2016/10/correlation_causation_example1-1200x565.png)
On the other hand, correlation is simply a relationship. Causation explicitly applies to cases where action A causes outcome B. The aims were to demonstrate why an inference of geomorphic causation from apparently well-performing models is invalid under common landslide data bias conditions (Model 1), to test a novel bias-adjustment approach (Model 2) and to exploit the underlying data bias to model areas likely affected by potentially damaging landslides (Model 3 intervention index), instead of landslide susceptibility. While causation and correlation can exist at the same time, correlation does not imply causation. The created models represent three conceptually different strategies to deal with biased landslide information. This study was built upon landslide information that systematically relates to damage-causing and infrastructure-threatening events in South Tyrol, Italy (7400 km 2). Literature shows that implications of such data flaws are frequently ignored. underrepresentation of landslides far from infrastructure or in forests) and does therefore not perfectly represent the spatial distribution of past slope instabilities.
![causality does not imply correlation causality does not imply correlation](http://1.bp.blogspot.com/-1g5n4iTDvPc/T3txmiyB43I/AAAAAAAABR0/NLUnZt70eQk/s400/moonchicken.jpeg)
In most cases, however, the available landslide data is affected by spatial biases (e.g. Well-performing models are commonly utilized to identify landslide-prone terrain or to understand the causes of slope instability.
![causality does not imply correlation causality does not imply correlation](https://statisticalbullshit.files.wordpress.com/2017/09/correlation-does-not-imply-causation.png)
Data-driven landslide susceptibility models formally integrate spatial landslide information with explanatory environmental variables that describe predisposing factors of slope instability.