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nguyennghia0902/
uplift-criteo-kdd-logic

Name: uplift-criteo-kdd-logic
Availability: InStock

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Uplift Modeling — Criteo KDD Project

Mô hình ước lượng CATE (Conditional Average Treatment Effect) trên Criteo Uplift Dataset.

Models

Artifact	Description
`models/dr_real.joblib`	DR-Learner trained on real treatment
`models/cf_real.joblib`	Causal Forest trained on real treatment
`models/x_real.joblib`	X-Learner trained on real treatment (Best)
`models/dr_placebo.joblib`	DR-Learner placebo baseline
`models/cf_placebo.joblib`	Causal Forest placebo baseline
`models/x_placebo.joblib`	X-Learner placebo baseline
`models/propensity_model.joblib`	Propensity score model
`models/tau_dr_real.npy`	Predicted CATE array — DR-Learner
`models/tau_cf_real.npy`	Predicted CATE array — Causal Forest
`models/tau_x_real.npy`	Predicted CATE array — X-Learner

Key Results

X-Learner Uplift@20%: 0.01439 (4× vs average)
Optimal Budget Ratio: 5% (Causal Forest)
Uplift ROI @ 20% Budget: 13.65% (vs 2.93% of Random Targeting)
Profit Gain vs Random @ 20%: +$11,995
Profit Gain vs Send All @ 20%: +$446,745

Usage

python

from huggingface_hub import hf_hub_download
import joblib, numpy as np

# Load best model 
x_model = joblib.load(hf_hub_download("nguyennghia0902/uplift-criteo-kdd-logic", "models/x_real.joblib"))

# Predict CATE
tau = x_model.effect(X_test_fe)