Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments
The article explores methods to accurately learn proxy metrics from historical experiments to improve long-term outcomes in technology companies.
MAIN POINTS
- Establishing proxy metrics' relationship with north star metrics is crucial for evaluating long-term outcomes.
- Naive approaches to correlating proxy and north star metrics can lead to misleading conclusions.
- Proposed estimators like TC, JIVE, and LIML help overcome biases in measuring proxy metrics.
TAKEAWAYS
- Accurate proxy metrics are essential for decentralized experimentation environments like Netflix.
- Linear models of treatment effects facilitate better coordination and innovation in metric development.
- The research emphasizes the need for flexible data architecture to streamline method applications.