Weekly Digest #85

Articles

Using Back-Door Adjustment Causal Analysis to Measure Pre-Post Effects

The mobile web platform’s conversion rate 14 days before the bug fix is the control, the 14 days following the fix is the treatment, and the conversion rates on other platforms serve as the covariates.

Challenges

• We can’t identify all confounders. Sometimes, we don’t know what the confounding variables are or we can’t capture all major confounders.
• We can’t choose the right list of covariates and validate the impact of the chosen covariates.

Solutions

• We can brainstorm potential confounding effects before measurement to make numerous strong hypotheses
• we can use advanced methods such as the instrumental variables method or the regression discontinuity design method to achieve an unbiased estimate despite being unable to block all the back-door paths

The Journey to Cloud Development: How Shopify Went All-in on Spin

Taking Dev Environments to Kubernetes Pods