March 15, 2019
degree of association or relationship between the observed values taken by two variables (\(X\) and \(Y\))
To infer causality from correlation, need to know what problems we have to assume are absent
bias (spurious correlation, confounding): \(X\) and \(Y\) are correlated but the correlation does not result from causal relationship between those variables
random association: correlations between \(X\) and \(Y\) occur by chance and do not reflect
confounding occurs when some other variable \(W\) is causally linked to \(X\) (independent variable) and \(Y\) (dependent variable).
If we diagram causal links between variables using this notation: \(X \to Y\) implies \(X\) causes \(Y\), then…
confounding occurs when there is a path between \(X\) and \(Y\) that is non-causal (goes the "wrong way" on at least one arrow)
Anti-refugee hate speech on social media causes anti-refugee violence?
hate-speech \(\to\) increase perceived threat \(\to\) decrease acceptance of refugees \(\to\) violence perceived as justified \(\to\) violence
IV: exposure to anti-refugee statements on Facebook; DV: incidents of violence against refugees
Increase exposure to anti-refugee content online associated with increase in violence against refugees
Test this hypothesis in Germany (2015-2017):
Are they correlated?
Can you infer causality?