TLDR
In a world full of data, we can understand the impact of impact with clever methods. Meet Granger causality: cause precedes effect. The method assumes stationarity of the data - data free from time-related biases. The main difference from a standard experiment is the level of certainty that the observed effect is really something. Natural experiments and granger causality are alternatives and could be classified as quasi-experimental approaches for time-series data. For instance, we could answer a question of whether a rise in interest in heat waves preceeds increased interest in climate change.via the TL;DR App
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Written by nikolao | Combines ideas from data science, humanities and social sciences. Views are my own.
