# Statistics - Causation - Causality (Cause and Effect) Relationship

Cause and Effect Relationship.

Nothing beats a simple, elegant, controlled, randomized experiment if you want to make strong claims about causality.

Causal inference is a difficult and slippery topic, which cannot be answered with observational data alone without additional assumptions.

Causation comes generally from directed research. From the raw data, you got generally a correlation but not a causation. An other approach is to say that if X causes Y, then the noise affecting X will also affect Y.

## Requirements

Strong causal claims require:

• Random and representative samples
• No confounds (impossible)

## Documentation / Reference

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