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

  1. When the causal impact to be tested is not in companies control.
  2. When establishing a Control may incur too large an opportunity cost since they do not receive the Treatment. For example, A/B experiments can be costly for rare events, such as establishing the impact of running ads during the IPL.
  3. There is legal or ethical issues-holding information from a user about the side effects of medicine being currently tested.
  4. When proper randomization is tough to perform.
  5. It is not technically feasible to perform an A/B test.
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.
  1. Interrupted Time Series
Output of the library
Correlation matrix between fruitchop and other games
cointegration P-values




Connecting the Dots.

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Aashay Sachdeva

Aashay Sachdeva

Connecting the Dots.

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An user flow chart. At the beginning is is a user signup box, and then two paths branch from it. 1st path: user browses for pencils then user buys. 2nd path: user browses for crayons, then user buys. An arrow pointing to the user browses for crayons box, calling out that we’re A/B testing a buy-now button for crayon users only. Finally, there’s thinking-aloud clouds that asks the reader, “do i randomize where user signs up?” or “randomize where user browses for crayons?”