You can’t always A/B test, that is why you need to learn about Quasi Experiments

https://shopify.engineering/using-quasi-experiments-counterfactuals
One common type of confound is an unrecognized common cause. For example, in humans, palm size has a strong correlation with life expectancy: on average the smaller your palm, the longer you will live. However, the common cause of smaller palms and longer life expectancy is gender: women have smaller palms and live longer on average (about six years in the US). Ron Kohavi; Diane Tang; Ya Xu. Trustworthy Online Controlled Experiments (Kindle Locations 3396–3398). Cambridge University Press. Kindle Edition.
https://netflixtechblog.com/quasi-experimentation-at-netflix-566b57d2e362

The main goal of the algorithm is to infer the expected effect a given intervention (or any action) had on some response variable by analyzing differences between expected and observed time series data.

Data is divided in two parts: the first one is what is known as the “pre-intervention” period and the concept of Bayesian Structural Time Series is used to fit a model that best explains what has been observed. The fitted model is used in the second part of data (“post-intervention” period) to forecast what the response would look like had the intervention not taken place. The inferences are based on the differences between observed response to the predicted one which yields the absolute and relative expected effect the intervention caused on data.

Output of the library
https://shopify.engineering/using-quasi-experiments-counterfactuals
Correlation matrix between fruitchop and other games
cointegration P-values

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