Targeting retention offers with heterogeneous treatment effects
Causal ML
Uplift Modeling
Retention
A case-study template for deciding which customers should receive a retention intervention.
Decision Question
Which customers should receive a retention offer if the goal is incremental retention rather than high predicted churn?
Why This Is Causal
The highest churn-risk customers are not necessarily the customers most likely to respond to an intervention. A targeting policy should prioritize treatment effect, not baseline risk.
Identification Strategy
Use experimental assignment when available. If the data are observational, state the conditional exchangeability assumptions and evaluate overlap carefully.
Candidate Methods
- T-learner, S-learner, and X-learner baselines.
- Doubly robust learners.
- Causal forests for heterogeneous treatment effects.
- Policy value estimation for comparing targeting rules.
Notebook Plan
Add notebooks such as:
notebooks/01-targeting-problem.ipynbnotebooks/02-uplift-baselines.ipynbnotebooks/03-causal-forest.ipynbnotebooks/04-policy-value.ipynb
Decision Translation
Replace this section with a concise description of who should be treated, who should not be treated, and how much value the policy is expected to create.