In this blog post, we will look at different scenarios where the most used ML modeling techniques may misinterpret the real relationships in the data. Here we try to shift this paradigm to find actionable insights beyond spurious correlations based on estimating causal relationships and measuring the treatment effectiveness of Target KPI outcomes. 

Motivation for Causal ML

If we were given historic or observational data with 5% churned customers for a product in the last year, every business owner’s goal is to decrease this percentage by conducting a targeted campaign. We usually build a predictive classical Propensity Model of churn customers (Propensity score – Probability of churn given its covariates of customer behavior such as CLV, RFM, etc.) and prescribe discounts or upsell/cross-sell to customers by selecting thresholds.