Uplift Modeling Made Easy

Review modern uplift data, define id, dependent, treatment, and predictors, and explain splitting data into learning and validation samples to build and validate predictive models.

Explore uplift modeling methods, comparing separate test and control models with a one-model approach using interaction terms to optimize treatment impact and lift curves.

Explore three uplift modeling approaches, including decision-tree based and interaction-based methods, with stopping criteria to prevent overfitting and identify incremental responders for marketing treatment.

Transform data for modeling by removing high-missing values or identical variables. Then split into training and validation samples with a random seed and verify with a quick QC in SAS.

Explore how to generate and evaluate interaction effects in uplift modeling using SAS macros, comparing training and validation samples to assess stability of predictor–outcome relationships.

Demonstrates variable transformations for uplift modeling, handling missing data as zero, and exploring interaction effects to preserve relationships across training and validation samples.

Demonstrate variable transformations in uplift modeling by creating interaction variables with I_ prefix and transform variables with t_ prefix using a SAS macro. Apply process to training and validation data.

Explore stepwise regression and diagnostics in uplift modeling, using SAS output to assess convergence, null-hypothesis tests, residual chi-square, and to generate a model scoring equation with a SAS macro.

Present concise modeling results to both technical and non-technical audiences, covering business objective, analytical approach, expected impact, and model performance with final variables.