A causal inference problem faced by Uber

Many companies randomly assign a subset of their customers to treatment and control groups for running experiments to test discount strategies.

They send discount coupons to all the customers in the treatment group via email and track the difference between sales conversion rates of treatment and control groups.

On paper, this sounds straightforward. But in practice, the below four different behaviors of customers complicate this a great deal:

1. Defiers - This group will be negatively impacted upon receiving the coupon via email and will not make a purchase.

2. Always Takers - Despite being in the control group that did not receive the email, they will find a way to get their hands on the coupon and use the discount

3. Never Takers - They would choose not to open the email and hence, do not see the discount coupon. So they "choose" not to be treated.

4. Compliers - This group opens the emails and finds the discount coupon, i.e., they get treated. Companies ideally want their treatment and control groups to be filled only with these compliers.

Even if we overlook the Defiers and Always Takers, the presence of Never Takers in the treatment group is still a problem. Never Takers cause a selection bias, which goes against the core randomization principle of experimental design.

Uber and other companies employ a causal inference method known as the 'complier average causal effect (CACE)' to address this challenge

Check out Uber's blog post where they explain more about how they use different causal inference methods, including CACE, to improve user experience.

Comments

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