These are starting candidates. The final choice comes from your business metric, fair evaluation over time, inference cost, calibration and evidence about treatment effects. Only a model that improves the decision enters the production path.
| Case and data regime | Start with | Strong practical candidate | Add complexity when | Avoid |
|---|---|---|---|---|
| Small offer set, tabular customer data | Eligibility rules, no-offer control, logistic response baseline | CatBoost or LightGBM response model; randomised T/S-learner uplift and incremental-profit policy | Compare X/DR learners or causal forests when arm sizes, heterogeneity and overlap support them | Ranking by response probability and calling it incrementality |
| Large product/offer catalog, interaction history | Popularity/content candidates plus eligibility | Two-stage retrieval and ranking; LambdaMART/boosted ranker | Two-tower retrieval when interaction volume and catalog scale justify learned embeddings | Treating logged non-clicks as unbiased preference labels |
| Demand/pricing with adequate exposure data | Seasonal/current-price policy; log-price Poisson/negative-binomial GLM | CatBoost or LightGBM demand challenger plus an approved-grid profit optimiser | Flexible elasticity or DML/causal forest after identification; limited bandit exploration after logging and policy approval | Regressing historical selling price and presenting it as an optimal price |
| Fixed-horizon churn with mature labels | Recency rule and logistic regression | Calibrated CatBoost or LightGBM, evaluated at contact capacity | Better features or another learner only after realistic error analysis | Using accuracy or a universal 0.5 cutoff |
| Churn timing with censored follow-up | Kaplan–Meier cohort summary and Cox baseline | Random survival forest or survival boosting | Non-proportional hazards/time-varying approaches when diagnostics require them | Labelling customers with unfinished follow-up as non-churners |
| Retention intervention with randomised actions | No-contact control and average treatment effect | Uplift/DR model plus expected incremental retained contribution | Multi-arm treatment policy when there is enough support per action | Assuming highest churn risk means highest save probability |
CatBoost's categorical-feature support makes it convenient for many customer tables. LightGBM is another candidate when its training, memory and serving behaviour suit the data. Neither wins by default, so compare on the same splits and cost budget. Current tabular results depend on model version and evaluation protocol. Compare BeyondArena’s June 2026 benchmark with the September TabPFN 3.5 report. The second reports aggregate gains but does not lead everywhere across full grouped, temporal and large-data slices, and its group-feature protocol differs. Chapter 3 explains how to make the comparison match deployment.
The guides explain the specific conditions behind each row. Read them before choosing a library.