Discovery quality mediation and durable user value

Causal Inference
Mediation
Recommendation Systems
Metrics
A mediation project asking whether broader discovery exposure creates future value directly or indirectly through same-day satisfaction depth.

Decision Question

How much of the effect of broader discovery exposure on longer-term user value flows through same-day satisfaction depth?

Causal Setup

  • Treatment is broader discovery exposure.
  • Mediator is same-day satisfaction depth.
  • Outcome is future user value and engagement.
  • The main measurement challenge is that CTR may miss durable satisfaction.

Methods

  • Metric construction and validation
  • Mediation estimands and assumptions
  • Direct, indirect, and total effects
  • Robustness and sensitivity checks
  • SEM-style and ML mediation extensions

Decision Takeaway

The project frames metric design as a causal problem. A good discovery metric should explain durable value, retention, and engagement quality, not immediate response alone.

Selected Figures

26 Final Effect Decomposition

27 Final Robustness Ranges

28 Final Advanced Model Comparison

Notebook Sequence

  1. Discovery Quality Problem Setup and EDA
  2. Metric Construction and Validation for Discovery Quality
  3. Mediation Estimands and Assumptions for Discovery Quality
  4. Direct, Indirect, and Total Effects for Discovery Quality
  5. Robustness and Sensitivity for Discovery Quality Mediation
  6. Advanced SEM and ML Mediation Models

Generated Artifacts

Limitations

These notebook-driven causal analyses should be read with their identification assumptions, support diagnostics, measurement choices, and sensitivity checks in view.