Validating Causal Models Is Hard—But Here’s a Trick

A practical way to validate causal targeting models for marketing, personalization, and more.

• Prediction models? Easy to validate. A picture is of a cat or a dog. ✅

• Causal models. Hard. We want to compare two parallel universes: what happens with and without a marketing campaign. But we can’t observe both. ❌

Enter causal targeting models, which predict how each person will respond to a campaign. These models have a unique advantage: the richness of individual predictions creates a structure we can use for validation.

The Trick

A good model should rank individuals well—those predicted to respond most should actually respond better. But how do we check that?

Here’s the simple recipe:

1. Sort and Group: Sort predicted individual treatment effects and split them into groups (e.g., top 10%, top 25%).

2. Compute Group Effects: Each group contains people who received the campaign (treatment) and those who didn’t (control). Compare their outcomes to compute the “true” effects for each group.

3. Validate: The group effects should “behave nicely.” For example, the top group should respond significantly better than everyone else, capturing heterogeneity in treatment effects.

Why This Matters

This method gives you a structured way to validate causal targeting models—even without access to counterfactuals. 

It’s essential for personalization:

• Marketing: Who should get the next campaign?

• Sustainability: Who responds to green initiatives?

Some Useful Links

• Want to try it yourself? Check out this notebook from Microsoft Research, the creators of EconML, one of GitHub’s top causal inference packages.

• The theory: Dive into Radcliffe’s 2007 paper, which proposed this approach.