Causal Inference for Business Experiments

Causal Inference for Business Experiments#

⏳ Loading Pyodide…

Because correlation is what interns discover, but causation is what executives pay bonuses for.

💡 Why Causality Matters#

Let’s start with the sacred business truth:

“Sales went up after the marketing campaign — therefore, the campaign worked!”

No, Karen. Maybe it was Christmas. 🎄

Causal inference is about knowing what actually caused something to happen — not just what happened around the same time as your KPI moved.

🧠 Causality vs Correlation#

Concept	Description	Business Example
Correlation	Two things move together	Coffee sales rise when it rains ☕🌧️
Causation	One thing makes the other happen	Discounts cause more sales (probably)
Spurious correlation	A third factor fools you	Ice cream sales and shark attacks both rise in summer 🦈🍦

If you build a model on correlation alone, you might end up recommending to “launch shark-safe ice cream ads” — which is, admittedly, very on-brand for modern marketing.

🧪 The A/B Test – Business Edition#

A/B testing is the most popular way to establish causality — or at least pretend to.

A/B Test = Controlled chaos with statistical backing.

Example:#

Group A: sees the old website
Group B: sees the new “AI-enhanced” website that nobody understands

If B converts better → great, causality achieved. If not → you just proved your designer wrong (scientifically).

Basic Code Example (with Statsmodels)#

🧩 Confounders: The Hidden Villains#

A confounder is something that affects both your independent and dependent variables. They sneak in like spies and ruin your experiment. 🕵️‍♀️

Example:

You find that “People who buy organic food also buy more yoga mats.”

Confounder: Income.

Rich people can afford both quinoa and flexibility.

To fix this, we use matching, stratification, or causal graphs (DAGs) to isolate the real relationship.

📈 DAGs – Drawing the Blame Network#

A Directed Acyclic Graph (DAG) is basically a corporate blame chart:

Arrows = “This thing affects that thing.”
Goal = Find out who’s really responsible for the KPI going up or down.

Marketing Spend → Sales
     ↑
     └── Seasonality

Moral: Sometimes, it’s not your campaign. It’s summer vacation.

🔍 Regression for Causal Estimation#

Regression can estimate causal effects — but only if you’re careful.

Here, C(season) helps control for the confounder — so we’re not blaming your marketing budget for what Santa Claus did. 🎅

🧮 Causal Inference Frameworks#

Method	Description	Use Case
A/B Testing	Randomized controlled experiment	Website design, pricing
Difference-in-Differences (DiD)	Compares changes before/after treatment	Policy, region-based campaigns
Instrumental Variables (IV)	Uses an external “randomizer” variable	Ad exposure, market shocks
Propensity Score Matching	Matches treated vs. control with similar features	Customer-level analysis
Causal Forests / DoWhy / EconML	Machine learning for causal inference	When you want causality and flexibility

🏢 Business Example: Email Campaign Impact#

A retailer sends promotional emails to half their customers. After 2 weeks:

Group A (email): +15% sales increase
Group B (no email): +12%

The intern says:

“Emails work! +3% lift!”

But… customers who got emails also had higher previous spending.

After controlling for customer value, the real lift? Barely +0.5%.

Moral: Data never lies — but analysts often forget context. 😅

🧪 Mini Exercise#

Try designing a simple causal test:

Pick a recent business change (e.g., new pricing, feature launch).
Split users randomly.
Measure a KPI (conversion, retention).
Use a t-test or regression to estimate the lift.
Report your findings with confidence intervals — and jokes.

💬 TL;DR#

Correlation ≠ Causation (unless you’re writing a bad investor deck).
Randomization is your best friend.
Always watch for confounders — they’re everywhere.
A/B testing is simple but powerful.
Use causal ML if you want to sound fancy and get that “AI Strategy” budget. 💰

# Your code here