No Interference, No Ambiguity: The SUTVA Assumption

Your randomized experiment is clean. The groups are balanced and comparable. The p-value is significant. But behind the scenes, the treatment is leaking. User A shared their referral link with User B in the control group, and suddenly your "independent" comparison is contaminated.

Welcome to the most common—and most ignored—failure point in experimentation: SUTVA (The Stable Unit Treatment Value Assumption). As Fisher famously warned, consulting a statistician after a broken experiment is just asking for a "post-mortem". If SUTVA breaks, you get confident numbers that mean absolutely nothing.

In this episode, we discuss:

• The Two Pillars of SUTVA: Why valid experiments require both "No Interference" (no spillovers) and "Consistency" (a well-defined treatment).

• The Three Mechanisms of Interference: From direct network effects to indirect marketplace competition and systemic behavioral redirection.

• The Consistency Trap: Why "the feature" can't mean different things for different users—and how to avoid "hidden versions" of your intervention.

• The Experimental Fix: When to move beyond individual randomization toward Cluster Randomization, Switchbacks, or Synthetic Controls.

The Interference Diagnostic (Key Takeaways):

• Contagion Risks? Randomize at the city or region level to keep social interactions within the treated unit.

• Marketplace Bottlenecks? Use switchback designs to handle units competing for finite supply.

• Spatial Shifts? Use buffer zones or synthetic controls to ensure you aren't just claiming credit for demand that simply moved elsewhere.

Stop running experiments that leak. Start designing for stability.

📖 Read the companion deep dive (with illustrations and takeaways): https://inferenceintel.substack.com/p/no-interference-no-ambiguity-the

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 causal inference.

🤝 Connect with me on LinkedIn: https://www.linkedin.com/in/linjia/

🚀 Support the Craft

If you found this episode valuable, please consider:

• Following the Podcast: Tap the "+" or "Follow" button on Spotify to never miss a deep dive into causal inference and GenAI.

• Sharing the Episode: Know a Data Scientist or Product Leader struggling with "No Overlap"? Send this their way.

• Joining the Conversation: Share your thoughts on today’s topic on LinkedIn—let’s raise the standard of the DS craft together.

  
  

No Overlap, No Answer: The Positivity Assumption

A causal effect can only be estimated where a comparison is actually possible. Imagine evaluating a loyalty program where every enterprise customer is already enrolled—leaving you with no unenrolled counterparts to compare against. This is a violation of Positivity. While exchangeability requires that groups are comparable, positivity requires that the comparison actually exists.

In this episode, we discuss:

• Structural vs. Random Violations: Why business-logic "zeros" cannot be fixed with more data.

• The Propensity Score Plot: How to visually verify if your treated and untreated groups cover the same territory.

• The Trimming Trade-off: Why discarding extreme observations to force overlap changes the population your results apply to.

The Positivity Audit (Key Takeaways):

• Verify Overlap: Use propensity scores to ensure groups share common support.

• Identify Structural Zeros: Recognize when policy or logic makes receiving a treatment impossible for certain subgroups.

• Watch External Validity: Always report dropped observations to clarify the narrowed scope of your findings.

🚀 Support the Craft

If you found this episode valuable, please consider:

• Following the Podcast: Tap the "+" or "Follow" button on Spotify to never miss a deep dive into causal inference and GenAI.
• Sharing the Episode: Know a Data Scientist or Product Leader struggling with "No Overlap"? Send this their way.
• Joining the Conversation: Share your thoughts on today’s topic on LinkedIn—let’s raise the standard of the DS craft together.

📖 Read the companion deep dive (with illustrations and takeaways): https://open.substack.com/pub/inferenceintel/p/no-overlap-no-answer-the-positivity?r=7bs4uy&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

🤝 Connect with me on LinkedIn: https://www.linkedin.com/in/linjia/

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.

Comparing Apples to Apples: The Exchangeability Assumption

Your dashboard flags a troubling trend: users who contacted customer support have a 40% higher churn rate than those who didn’t. The immediate takeaway seems obvious—support is failing.

But is it? Or did those customers contact support because something had already gone wrong?

In this episode, we tackle the heart of the "Bad Comparison" problem. We dive into Exchangeability—the fundamental assumption that allows us to treat observational data as if it were a randomized experiment. If your groups aren't exchangeable, your model isn't measuring an effect; it's just measuring a pre-existing difference.

In this episode, we discuss:

• The Support Trap: Why correlation often hides the "underlying fire" and leads to backward business decisions.
• The "Swap" Test: A simple mental framework to determine if your treatment and control groups are truly comparable.
• Bias Under the Null: How a model can show a massive "effect" even when the treatment does absolutely nothing.
• Forcing Exchangeability: The role of conditioning and why choosing the right covariates is the most critical decision a Data Scientist makes.

Stop settling for bad comparisons. Start ensuring your data is "exchangeable" before you trust the result.

📖 Read the companion deep dive (with illustrations and takeaways): ⁠https://inferenceintel.substack.com/p/comparing-apples-to-apples-the-exchangeability⁠

🚀 Support the Craft

If you found this episode valuable, please consider:

• Following the Podcast: Tap the "+" or "Follow" button on Spotify to never miss a deep dive into causal inference and GenAI.
• Sharing the Episode: Know a Data Scientist or Product Leader struggling with the "Data Validity Cliff"? Send this their way.
• Joining the Conversation: Share your thoughts on today’s topic on LinkedIn—let’s raise the standard of the DS craft together.

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 causal inference.

🤝 Connect with me on LinkedIn: https://www.linkedin.com/in/linjia/

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/⁠

Headline: Why your "Perfect" Models are failing the Boardroom.

For months, the team worked on the model. The AUC was 0.92. The validation sets were clean. But when you shipped it to the real world? Nothing happened. The metrics didn't move. The business didn't grow.

Well, you didn't have a "data" problem. You had a Causality problem.

You just hit the Data Validity Cliff—the point where your high-tech models are great at "seeing," but completely blind at "doing."

In this episode, we break down Judea Pearl’s Ladder of Causation to fix your strategy:

• The Selection vs. Policy Trap: Why finding users who love you isn't the same as making users love you.

• The Owl vs. The Scientist: Why "superpowered observation" is a dead end for decision-making.

• The UK-to-US Simulation: How to "transport" data across markets without a new pilot.

Stop being just an observer. Start becoming a simulator.

📖 Read the companion deep dive (with diagrams & checklists) : ⁠⁠https://inferenceintel.substack.com/p/ladder-of-causation-how-to-upgrade

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/⁠

The Infinite Data Trap: Why More Data Won't Save Your Causal Models

You have petabytes of user data. Your model has 99% validation accuracy. But when you ask it, "What happens if we change our strategy?", it gives you an answer that is confidently wrong.

Welcome to the Infinite Data Trap. In this episode, we reveal why "big data" is useless for decision-making without a critical, often neglected step: Identification.

We break down the "Bridge to Causal Truth" and explain why identification is the only thing standing between a reliable insight and misinformation masquerading as truth.

In this episode, we discuss:

Identification vs. Estimation: Why moving from a "what-if" question to a concrete number requires a bridge of assumptions—not just a better algorithm.

The E-commerce Blindspot: A real-world look at how unobserved user engagement can make a promotional email look like a success when it’s actually a bias.

Beyond Correlation: How to be explicit about your assumptions (like "no interference") and why sensitivity analysis is your best defense against being "confidently wrong."

Stop gathering data. Start identifying effects.

📖 Read the companion deep dive : ⁠

https://inferenceintel.substack.com/p/the-bridge-to-truth-why-identification

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 causal inference.

Connect with me:

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

The Ranking Trap: Why PLR Fails with Multiple Treatments

You’re testing four different promotional strategies—a discount, free shipping, BOGO, and loyalty points. You run a Partially Linear Regression (PLR), get a clean table of coefficients, and rank them to decide your next big investment.

There’s just one problem: Your ranking is likely a lie.

In this episode, we dive into a dangerous "trap" in observational causal inference. We explore why regression-style estimators like PLR don't actually estimate the Average Treatment Effect (ATE) when effects are heterogeneous. Instead, they estimate a variance-weighted average that can completely reverse your treatment rankings.

In this episode, we discuss:

• The Overlap Weighting Problem: Why PLR only "learns" where it has leverage, and how that creates a different subpopulation for every treatment you test.

• PLR vs. IRM: Why the Interactive Regression Model (IRM) is the essential choice for multiple discrete treatments.

• The Bridge to DoubleML: How to use machine learning to model "nuisance" components while maintaining statistical rigor.

• RCT vs. Observational Realities: Why PLR works in a randomized trial but fails in the "wild" of marketplace data.

Stop letting overlap redefine your estimand. Start using the right model for the job.

📖 Read the companion deep dive : ⁠ ⁠

https://inferenceintel.substack.com/p/launching-the-inference-and-intelligence

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/⁠

The Recipe for Causal Truth: Estimand, Estimator, and Estimate

You have a dataset, a model, and a final number. But can you explain—with precision—what that number actually represents?

In the world of causal inference, precision is everything. We often use terms like "the result" or "the algorithm," but failing to distinguish between the What, the How, and the Result is where most analytical errors begin.

In this episode, we break down the three fundamental pillars of the estimation process: the Estimand, the Estimator, and the Estimate. Using a simple baking analogy, we demystify these academic terms and turn them into a practical framework for your daily data work.

In this episode, we discuss:

• The Estimand (The "What"): Why defining your theoretical "ideal cake" is the most important step before touching any data.

• The Estimator (The "How"): How your recipe—the algorithm or function—transforms raw inputs into insights.

• The Estimate (The "Result"): Understanding the concrete number that comes out of the oven, and why it’s only an approximation of the truth.

• The Estimation Process: How to align these three trios to ensure your business questions are actually being answered by your code.

Don't just run models. Understand the ingredients of your causal claims.

📖 Read the companion blogpost : ⁠⁠⁠ ⁠⁠

https://inferenceintel.substack.com/p/causal-inference-from-the-ground

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/⁠