You can never see the data you need most to make a decision. 📉

It sounds counterintuitive, but the core of Causal Inference isn't just math—it's imagination. 🌌

Most Data Science focuses on predicting the future based on what happened in the past. But Causal Inference asks a much harder question: What would have happened if we had acted differently?

In Part 4 of my series, "Causal Inference from the Ground Up," I dive into the Potential Outcomes Framework—the bedrock of how we define "cause" in a world where we can't see parallel universes.

The "Discount Trap" 💸

Imagine your retention team sends a 20% discount to at-risk subscribers. Churn drops. Everyone celebrates.

But then comes the uncomfortable question: Were those customers actually going to churn, or were they going to renew anyway? If they were going to stay regardless, you just gave away 20% of your revenue for nothing.

This is the Fundamental Problem of Causal Inference. We observe the outcome of the treatment, but the counterfactual—whether that same customer would have renewed without the discount is forever hidden from us.

In this deep dive, I break down:

• The 3 Primitives: Units, Treatments, and Potential Outcomes.
• Individual Treatment Effect (ITE): Why it’s the "holy grail" of decision-making.
• The Baking Example: A simple way to visualize unobserved realities.
• The ATE Solution: How we use population averages to "cheat" the fundamental observation gap.

If you’re moving beyond simple prediction and into the world of high-stakes decisions, understanding Potential Outcomes is the first step toward true rigor.

📖 Read the companion deep dive : https://inferenceintel.substack.com/p/the-data-youll-never-see-understanding

About the Host

Lin Jia is a Senior Data Scientist and Craft Lead at Booking.com with over 9 years of experience. Operating at the intersection of statistical inference, causal machine learning, and GenAI evaluation, she specializes in building the frameworks that enable trustworthy, decision-ready insights under real-world constraints. A recognized expert in the field, Lin has authored research on sensitivity analysis presented at KDD 2024 and leads the development of organization-wide standards for experimentation and observational causal inference.

Connect with me:

• Lin on ⁠⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/in/linjia/⁠⁠
• Join Inference & Intelligence Lab biweekly ⁠⁠https://inferenceintel.substack.com/⁠