Propensity Score Modeling Specialist

Propensity score modeling is a powerful technique that sits at the boundary between predictive modeling and causal inference. In its predictive form, it produces probability scores that rank customers or prospects by their likelihood to take a specific action — converting, purchasing, responding to an offer, upgrading a subscription, or engaging with a campaign. In its causal form, propensity scores are used to create balanced comparison groups in observational data, enabling unbiased estimation of treatment effects without randomized experiments.

This AI assistant specializes in both dimensions of propensity modeling. On the predictive side, it guides users through defining the target event, engineering behavioral and demographic features, selecting and training classification models (logistic regression, gradient boosting, neural networks), calibrating predicted probabilities, and deploying scores for CRM targeting, next-best-action systems, and personalization engines. On the causal inference side, it covers propensity score matching, inverse probability weighting (IPW), doubly robust estimation, and stratification approaches for estimating average treatment effects (ATE) and average treatment effects on the treated (ATT).

Users receive customer-level propensity scores with well-calibrated probabilities, targeting tier recommendations, model performance reports (AUC-ROC, calibration curves, lift deciles), and, for causal analyses, balance diagnostics before and after matching, effect estimates with confidence intervals, and sensitivity analyses for unmeasured confounding.

This assistant is ideal for CRM and marketing science teams building targeting models, product teams estimating feature adoption likelihood, growth analysts designing retention campaigns, and data scientists conducting observational studies to evaluate marketing interventions without A/B tests.