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PTA 403 · Unit 4 of 6

Experimentation and Causal Inference

Product Analytics and Data-Informed Decisions

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Learning objectives

After completing this unit, you will be able to:

  • Apply experimentation and causal inference to real product and technology decisions
  • Apply the frameworks in "Experimentation and Causal Inference" to a real management decision
  • Make progress on your Unit lesson exercises applied project

Why this matters

Broken experiments drive wrong ship/kill calls in weekly culture.

Lesson

Unit overview

Complete all 4 lessons in order. Each lesson follows the program gold standard: SignalStack scenarios, worked examples, practice problems, and managerial judgment prompts tied to Experimentation and Causal Inference.

Connection to applied work

This unit feeds directly into Unit lesson exercises. As you read, capture notes, examples, and data you can reuse in that deliverable. Strong students finish each unit with a draft section of their project, not just highlights.

Practice

  1. Write a one-page summary of this unit in your own words without looking at the lesson.
  2. Find a real company example (public filing, news article, or personal experience) that illustrates the main concept.
  3. Draft one paragraph recommending an action a manager should take based on this unit.
  4. Add at least three terms from this unit to your course glossary.

Knowledge check

Answer these without notes before marking the unit complete:

  1. What is the central idea of "Experimentation and Causal Inference"?
  2. What mistake do beginners most often make when applying this material?
  3. How does this unit help you complete Unit lesson exercises?
  4. What is one decision you face this month where this unit applies?

Key takeaways

  • Apply experimentation and causal inference to real product and technology decisions
  • Business concepts only matter when they change a decision.
  • Your PTA 403 assessment (Experimentation and Causal Inference for Product Analytics and Data-Informed Decisions using SignalStack applied examples.) rewards applied understanding, not memorization.

Unit assessment

Complete each section below. Score 80%+ on the quiz to finish this unit's assessment.

50% applied project30% case work20% knowledge checks

Exercises

Apply what you learned in this unit with structured practice.

ExerciseApplied practice: Experimentation and Causal Inference45 min
Complete a focused practice exercise on **Experimentation and Causal Inference**. 1. Choose a real company, product, or situation you know. 2. Apply one core framework from this unit to analyze it. 3. Write your analysis in 300–500 words with a clear recommendation. 4. Cite at least one credible source.

Deliverable

300–500 word analysis document saved to your portfolio under PTA 403.

Rubric

  • • Framework applied correctly (not just named)
  • • Specific evidence from a real example
  • • Clear recommendation with tradeoffs acknowledged
  • • Professional writing with source citation
ExerciseDrill: Experimentation and Causal Inference30 min
Work through the practice problems in the unit lesson without looking at notes. Then check your work against the lesson and write a short reflection: - What you got right - One mistake you caught - One concept to review before the next unit

Deliverable

Problem solutions + 150-word reflection in your PTA 403 workbook.

Rubric

  • • Attempted all practice items before checking answers
  • • Honest reflection on errors
  • • Identifies a specific review action

Case analysis

Analyze a case using frameworks from this unit.

CaseCase analysis: Experimentation and Causal Inference60 min
Analyze a real business case through the lens of **Experimentation and Causal Inference**. Choose a public company event, HBR-style case, or documented decision. **Deliverable structure:** 1. Situation summary (150 words) 2. Analysis using this unit's frameworks (400 words) 3. Recommendation (150 words) 4. Risks and what would change your mind

Deliverable

2-page case write-up in your portfolio.

Rubric

  • • Case facts are accurate and sourced
  • • Analysis uses unit frameworks explicitly
  • • Recommendation is justified with tradeoffs
  • • Risks are specific, not generic

Knowledge quiz

Check your understanding before marking the unit complete.

1. SignalStack's Maya Chen faces: "Feature flag rollout showed +8% WAD but SRM alarm fired on team assignment." Which decision frame is strongest?

2. Which term from Experimentation and Causal Inference is defined correctly for SignalStack?

3. Priya Nair reviews evidence for Experimentation and Causal Inference. Which label is appropriate after 12 customer interviews showing a recurring theme?

4. Which mistake is most common when applying Experimentation and Causal Inference at dev-tools scale?

5. SignalStack reports $18M ARR and 92,000 DAU. Why does Experimentation and Causal Inference discipline matter at this scale?

6. Which framework mapping fits Experimentation and Causal Inference at SignalStack?

7. Dev Okonkwo warns about inference cost and reliability. Which guardrail metric fits Experimentation and Causal Inference?

8. Integrating PTA 403 for SignalStack, strongest next test after descriptive dashboard read is usually:

Next unit: Segmentation, Forecasting and Decision Models →