Home▸AI & Machine Learning▸Uplift Modeling Lab 2D — Persuadables, Sure Things & Sleeping Dogs

Uplift Modeling Lab 2D — Persuadables, Sure Things & Sleeping Dogs

2D companion to the 3D uplift-modeling simulator: a flat control-vs-treated conversion scatter that sorts a synthetic customer population into Persuadables, Sure Things, Lost Causes and Sleeping Dogs, with a live targeting threshold, incremental conversions and Qini coefficient.

AI & Machine Learning2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

Details

CategoryAI & Machine Learning

RenderingCanvas 2D

DifficultyAdvanced

Target FPS60

ControlsSliders: population size (80-900, default 400), persuadable share (0-60%, default 30%), sleeping-dog share (0-30%, default 10%), predicted-uplift targeting threshold (-30% to 60%, default 10%), model noise vs true uplift (0-40%, default 8%); Resample population button draws a fresh synthetic customer set.

uplift modelingcausal inferencemarketing analyticsmachine learningtargetingT-learner

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This is the 2D companion to the 3D uplift-modeling scene, computing the identical causal-inference math on a flat chart: a synthetic customer population plotted as a live scatter of p(convert|control) against p(convert|treated), colored into the four uplift quadrants — Persuadables, Sure Things, Lost Causes and Sleeping Dogs. Set a predicted-uplift targeting threshold and a model-noise level to see how a real two-model (T-learner) uplift model would rank and select customers, and watch the true incremental conversions, sleeping-dog backfire, and Qini coefficient update live as you change the campaign's targeting policy.

⚙ Under the hood

uplift modelingcausal inferencemarketing analyticsmachine learningtargetingT-learner

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install