Causal Machine Learning for Growth: Loyalty Programs, LTV, and What to Do When You Can’t Experiment
Last Updated on August 29, 2025 by Editorial Team
Author(s): Torty Sivill
Originally published on Towards AI.
A hands-on guide to replacing A/B tests with counterfactual reasoning
It’s clear that causal inference is becoming increasingly critical. Companies that have relied heavily on experimentation are now facing situations where A/B testing is either infeasible, impractical or even counterproductive. They’re looking for alternative ways to derive causal insights from the data they already have.
The article discusses the challenges of relying solely on traditional A/B testing for assessing loyalty program impacts on customer lifetime value (LTV). It outlines the importance of causal inference as an alternative that leverages historical data to estimate outcomes without experimental setups. The author illustrates how to reconstruct counterfactual scenarios using causal graphs and discusses the limitations of causal discovery methods, calling for better tools and approaches to enhance causal inference accuracy in complex environments.
Read the full blog for free on Medium.
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