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Module 6, Week 1: Real-World Applications

Article 12 of 13•20 min read

📊 Bringing It All Together

Let's explore how causal inference methods are applied in practice across different domains—from tech platforms to healthcare and public policy.

Table of Contents

  1. 1. A/B Testing at Scale
  2. 2. Causal ML in Tech
  3. 3. Healthcare & Medicine
  4. 4. Policy Evaluation
  5. 5. Key Takeaways
  6. 6. Next Week Preview

1. A/B Testing at Scale

Modern tech companies run thousands of A/B tests simultaneously. Key challenges include:

  • Network effects: Treatment spillover between users
  • Multiple testing: False discovery rate control
  • Heterogeneous effects: Personalization opportunities
  • Long-term effects: Short-term metrics vs long-term impact

Example: Recommendations A/B Test

Testing a new recommendation algorithm requires careful consideration of network effects, novelty effects, and long-term user engagement beyond immediate clicks.

2. Causal ML in Tech

Applications of causal ML in technology:

  • Advertising: Incremental ROI, attribution, ad targeting
  • Search & Ranking: Click attribution, position bias correction
  • Recommendations: Treatment effect heterogeneity for personalization
  • Pricing: Price elasticity, dynamic pricing strategies
  • Product Features: Feature impact on retention and engagement

3. Healthcare & Medicine

Causal inference is critical in healthcare for evidence-based medicine:

  • Treatment effectiveness: RCTs and observational studies
  • Personalized medicine: Heterogeneous treatment effects (CATE)
  • Drug safety: Adverse event detection in EHR data
  • Resource allocation: Hospital capacity, triage decisions

Ethical Considerations:

Healthcare applications require rigorous validation, interpretability, and fairness considerations. Model errors can have life-or-death consequences.

4. Policy Evaluation

Governments and NGOs use causal inference to evaluate interventions:

Example Applications:

  • Education policy: Class size effects, curriculum changes
  • Labor economics: Job training programs, minimum wage impacts
  • Public health: Vaccination campaigns, health interventions
  • Environmental policy: Pollution regulations, climate interventions

5. Key Takeaways

  • ✓Causal inference powers evidence-based decision making across industries
  • ✓A/B testing at scale requires handling network effects and multiple comparisons
  • ✓Healthcare and policy applications demand rigorous validation and ethical considerations
  • ✓Heterogeneous treatment effects enable personalization and targeted interventions

6. Next Week Preview

Module 6, Week 2: Validation & Deployment

In the final article, we'll cover model validation, sensitivity analysis, handling assumption violations, ethical considerations, and building production causal inference systems.

← Previous: Causal RLNext: Validation & Deployment →