Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models

written by Ross Chu and Shima Nassiri

The Causal Frontier: Measurement Beyond Randomization

The gold standard for determining the causal impact of a policy or product change at a company like Lyft is the A/B test (randomized experiment). By randomly assigning users to a treatment or control group, A/B tests inherently eliminate bias, providing clean estimates of the Average Treatment Effect (ATE). However, many critical business questions and large-scale initiatives simply cannot be randomized. This forces scientists to move past traditional experimentation and leverage quasi-experimental methods.