Causal Inference Basics: Correlation vs Causation and When A/B Tests Aren’t Enough 2026?

Correlation vs Causation, but business wants causal answers: “Will this feature cause retention to improve?” Causal inference gives you tools beyond A/B tests for messy, observational data.​

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Correlation vs causation pitfalls

Some classic examples:

• Ice cream sales increases Shark attacks (summer confounder)
• Gym membership increases Divorce rate (selection bias)

Solutions needed:

• Experiments (RCTs/A/B) when possible.
• Quasi‑experimental designs for observational data.
• Causal diagrams (DAGs) to spot confounders.​

Core causal frameworks

1. Potential outcomes (Rubin causal model):

• Y (1) = outcome if treatment
• Y (0) = outcome if control
• CATE = Y (1) – Y (0)

Challenge: can’t observe both for same unit.

1. Difference‑in‑differences (Did):
   Pre/post + treatment/control groups.
   Assumption: parallel trends absent treatment.​
2. Instrumental variables (IV):
   Instrument affects treatment but not outcome directly.
   Example: ad spend → clicks → purchases (if ad randomization).
3. Propensity score matching:
   Match treated/untreated on observables.
   Residual confounding risk.

When A/B tests fall short

You cannot run A/B tests with the below effects:

• Long‑term effects (lifetime value).
• Network effects (social products).
• Rare events (fraud, churn).
• External shocks (regulation changes).​

Quasi‑experiments fill the gap.

Practical example: feature rollout impact

Scenario: Rolled out “premium dashboard” to high‑value customers only. Did it reduce churn?

• Naive: Compare churn before/after → biased.
• Did: Compare treated vs similar control group, pre/post.
• Churn drop: 2pp treated, 0.2pp control → causal impact ≈ 1.8pp
• Validate parallel trends assumption with pre‑period plot.​

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Tools to start with

• Python: EconML, CausalML, DoWhy
• R: MatchIt, causalImpact
• Focus on assumptions + visualization over black‑box models.

Try this: Find a rollout (feature flag, pricing change). Build a Did analysis vs matched control. Check parallel trends plot.

Pinki Kumari

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