How deeply does Causal Inference for Data Science cover statistical confidence?

Explains its role in causal confirmation and sample-size trade-offs, but stops short of formal calculation or implementation.

Statistical confidence

As explained in Causal Inference for Data Science

A quantified degree of statistical assurance that an estimated result reflects a non-random effect under specified assumptions.

Statistical confidence is the degree of assurance that an observed difference reflects a real effect rather than random variation. In causal questions, randomized controlled trials and A/B tests provide the most reliable basis for establishing that confidence. section 1.3.4

Website design test

An A/B test may show that a new website produces more visits than the old design, supporting a causal difference with statistical confidence. If the design changes many things, the test cannot identify which change caused the result. If the change is small, detecting its effect may require a very large group. section 1.3.4

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

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Related concepts

confidencekind of · Confidence is a degree of certainty or assurance associated with a judgment, estimate, prediction, capability, or statistical procedure, often expressed as a score, probability, or coverage level.

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