1. Model Validation
Validating causal models is challenging because ground truth counterfactuals are unobserved. Strategies include:
- Placebo tests: Apply method to pre-treatment periods (should find no effect)
- Synthetic experiments: Simulate data with known ground truth
- Cross-validation: Split by units or time (not standard CV)
- Holdout RCTs: Validate on small randomized experiment
- Falsification tests: Test predictions that should be false if model is wrong
2. Sensitivity Analysis
Sensitivity analysis assesses how conclusions change under violations of key assumptions.
Key Questions:
- How strong would unobserved confounding need to be to change conclusions?
- What if parallel trends doesn't hold? (DiD)
- What if the instrument is weak or invalid? (IV)
- What if there's spillover between units? (SUTVA violation)
Rosenbaum Sensitivity Analysis:
Quantifies how large unmeasured confounding would need to be (in terms of odds ratios) to alter conclusions in matching studies.
3. Robustness Checks
Demonstrate robustness by varying:
- Model specification: Try different functional forms, controls
- Sample definition: Vary inclusion criteria, time windows
- Method: Compare multiple causal inference approaches
- Hyperparameters: Show stability across tuning choices
⚠️ Multiple Testing:
Running many robustness checks risks p-hacking. Pre-register analysis plans when possible.
4. Ethical Considerations
Causal inference has profound ethical implications:
- Fairness: Are treatment effects equitable across demographic groups?
- Privacy: Does inference require sensitive personal data?
- Transparency: Are assumptions and limitations clearly communicated?
- Harm prevention: Could incorrect estimates lead to harmful decisions?
- Algorithmic bias: Do models amplify existing inequalities?
Always consider who benefits and who might be harmed by causal inference applications.
5. Production Systems
Deploying causal models in production requires:
Infrastructure Considerations:
- Real-time inference: Low-latency prediction pipelines
- Model monitoring: Track covariate shift, prediction drift
- A/B testing: Validate causal models with controlled experiments
- Feature engineering: Automated feature computation and updates
- Model versioning: Track model lineage and reproducibility
- Explainability: Provide interpretable treatment effect estimates
Example Production Architecture:
# Pseudocode for production causal inference service
class CausalInferenceService:
def __init__(self):
self.propensity_model = load_model('propensity_v2')
self.outcome_model = load_model('outcome_v2')
self.feature_store = FeatureStore()
self.logger = Logger()
def predict_treatment_effect(self, user_id):
# Fetch features
features = self.feature_store.get_features(user_id)
# Predict propensity
propensity = self.propensity_model.predict(features)
# Predict potential outcomes
y0 = self.outcome_model.predict(features, treatment=0)
y1 = self.outcome_model.predict(features, treatment=1)
# Estimate ITE
ite = y1 - y0
# Log for monitoring
self.logger.log({
'user_id': user_id,
'ite': ite,
'propensity': propensity,
'timestamp': time.now()
})
return {'effect': ite, 'confidence': compute_ci(ite)}6. Series Conclusion
🎉 Congratulations!
You've completed the Causal Inference Series. We've covered everything from fundamental concepts (potential outcomes, DAGs) to advanced methods (causal forests, deep learning, causal RL) and practical applications.
Key Skills Acquired:
- ✓ Understanding when and how to apply different causal methods
- ✓ Implementing classical and modern causal ML algorithms
- ✓ Validating assumptions and assessing robustness
- ✓ Deploying causal models in production
- ✓ Navigating ethical considerations
Next Steps:
- Apply these methods to real-world problems in your domain
- Explore research papers for cutting-edge developments
- Contribute to open-source causal inference libraries
- Stay updated on new methods and best practices
"Causality is the scaffolding of reality—understanding it empowers us to change the world through principled intervention."