Randomization

As explained in Causal AI

Random assignment, or randomization, allocates units or intervention values to conditions according to a random mechanism.

Randomization assigns units or intervention values through a random mechanism. In causal analysis, random assignment is a stochastic intervention: a platform digitally flips a coin to set the target variable. section 7.5.1

Randomization removes incoming causal influence from unknown common causes. It can therefore protect an experiment when the assumed causal DAG is incomplete, even though an ideal intervention may already identify the effect under a correct DAG. section 7.5.1 section 7.7

Implementation has two layers:

• Choose outcomes according to a specified distribution; computers emulate this process with deterministic pseudorandom number generators initialized by a random seed. section 2.2.2
• Assign experimental units to conditions independently of causes that might otherwise affect both treatment and outcome. section 7.5.1

Online gaming experiment

A digital experimentation platform randomly assigns players to high- or low-engagement groups, then compares in-game purchases. Assigning the first players to one group and later players to another could fail if login time affects both engagement and purchases. section 7.5.1

Figure 7.12 Randomization eliminates incoming edges from unknown common causes.

Diagram showing how randomization eliminates incoming edges from unknown common causes. section 7.5.1

How Causal AI covers it

How deep it goes

Explains stochastic intervention, contrasts it with ideal intervention, and traces protection against unknown causes; implementation reaches pseudorandom sampling.

Examples

Uses a digital gaming experiment, player engagement groups, login timing, and pgmpy and Pyro sampling examples.

What it assumes

Assumes causal interventions and DAGs, while defining random generation and pseudorandom mechanisms explicitly.

What you can do after

Apply it via a randomized experimentation platform or seeded pseudorandom generator to assign intervention values.

How it connects

Connects stochastic intervention with causal DAGs, graph surgery, intervention operators, and unknown common causes.

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Also explained in

• 

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  Experimentation for EngineersRandomization is the assignment of observations or users to experimental versions by chance so that unrelated factors do not systematically differ between versions.
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  Causal Inference for Data ScienceRandomization is the random assignment of participants or units to experimental groups or treatment conditions.
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  Data Analysis with LLMsRandomization is the introduction of stochastic variation during generation so repeated generation attempts can produce different outputs.
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  liveBookmanning.com
  Ensemble Methods for Machine LearningRandomization is the deliberate introduction of stochastic variation into data sampling, feature selection, or model training.

Compare how 5 books explain randomization →

Related concepts

• study designused by · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
• property-based testingused by · Property-based testing is a software-testing technique that generates varied inputs and checks whether specified general properties of the software’s behavior hold across them.
• testuses · A test is a procedure or check that evaluates behavior using specified inputs or evidence against expected results or criteria.
• samplingimplemented by · Sampling is a procedure for selecting or generating observations, values, or outcomes from a larger population, signal, or probability distribution according to a specified method.
• measurement biasaddresses · Measurement bias is systematic distortion introduced when a measurement process or method produces results that misrepresent the true values or construct of interest.
• policykind of · A policy is a rule, strategy, or mapping that governs or determines decisions, actions, permissions, or behavior under specified conditions.

This explainer was generated by Manning's knowledge graph from the text of Causal AI. The section links point to the parts of the book it draws on.