Causal Inference from Event Streams

Correlation is easy to spot, but causation is what truly drives decisions. Causal inference aims to determine whether one event or action actually influences another – a critical step in moving from descriptive analytics to actionable intelligence.

Event Sourcing offers a unique advantage for causal analysis because it preserves the complete, ordered history of domain events, allowing you to reconstruct scenarios, identify temporal relationships, and test cause-and-effect hypotheses with precision.

Why Causality Matters

In business and AI, acting on correlation alone can be misleading. If late fees and increased borrowing frequency appear together, does one cause the other – or are both driven by a third factor, such as seasonal promotions?

Without understanding the direction and mechanism of influence, interventions risk being ineffective or even harmful.

Causal inference helps answer questions like:

• Did sending reminders actually reduce late returns?
• Did a policy change increase member retention?
• Did promoting certain genres change borrowing diversity?

How Event Sourcing Helps

Causality analysis depends on accurate sequencing and context. Event Sourcing ensures:

• Precise timelines – every event is timestamped and immutable.
• Full context – you can see not only what happened, but also the surrounding events.
• Counterfactual potential – by replaying history with "what if" changes, you can estimate alternative outcomes.

Because you can reconstruct exact historical states, you can apply causal inference techniques – such as difference-in-differences, propensity score matching, or causal graphs – on clean, reliable data.

Practical Example (Library Domain)

Suppose you introduce a two-day reminder for books due soon. Over time, late returns drop by 15%. Is this improvement caused by the reminder, or by other factors like shorter queues or seasonal trends?

With the full event history, you can:

1. Identify members who received reminders versus those who did not.
2. Control for confounding factors such as book type, season, or member category.
3. Compare late return rates between groups over the same time period.

This structured approach moves you from guessing to knowing.

AI/ML Considerations

Causal insights are especially valuable when training AI models:

• They help identify features that actually influence outcomes, improving model interpretability.
• They support policy evaluation, ensuring interventions are backed by evidence.
• They enable simulation of potential changes before deploying them in production.

By feeding causally validated features into models, you reduce noise and bias, leading to more stable and trustworthy predictions.

Best Practices

• Always combine domain expertise with statistical methods to avoid false conclusions.
• Use multiple causal inference techniques to validate findings.
• Maintain clear event definitions so causal relationships are not obscured by inconsistent semantics.
• Treat causal inference as an iterative process – new events and policies can shift relationships over time.

With Event Sourcing, you have not just the data but the narrative of your domain – making it possible to uncover the true drivers behind your metrics and design interventions that reliably change outcomes.

Next up: Designing Feedback Loops for Human-in-the-Loop AI – integrate human judgment into AI systems for greater accuracy, accountability, and trust.