Script · Deliverable

Uplift models,
benchmarked at scale.

PythonUpliftCausal Forest7M rows

A reproducible benchmark on the public 7M-row Criteo uplift dataset: S-, T-, X-, DR-Learner and Causal Forest against a naive response-ranking baseline, all judged with AUUC-based validation.

01 — WHAT'S INSIDE

Built to hand over and run.

01

Six methods

S/T/X/DR-Learner, Causal Forest, and a naive baseline, head to head.

02

7M rows

Run at real scale on the public Criteo dataset.

03

AUUC validation

Judged by uplift-appropriate metrics, not raw accuracy.

Need this adapted to your data?

I ship it tested, documented, and ready to run in your stack.

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