The intervention causes the variable to become unrelated to factors that usually affect it so the arrows between these factors and the variables will not exist in the causal diagram with the intervention.

Intervention in Causal Diagrams

In the context of Berkeley's admission paradox, Bickel contends that discrimination is to do with making decisions that are influenced by the sex of the applicant, even if it is immaterial for the admission decision.  The words like "exercise of decision," "influence," and "immaterial" are suggestive of causation, either belonging to the second (intervention)or third (counterfactuals) Ladder of Causation.  

Bickel's Definition of Discrimination

Intervention questions ask about the outcomes of actively changing the environment or taking a specific action (e.g., "What will happen if we change the environment?" or "What if we do...?"). Examples of intervention questions include: "What is the probability we get an A on our next Python exam given we increase our daily study hours?" ($$ P(AGrade| do(hours)) $$) and "How many more daily hours should I dedicate to exam study?"

Examples of Intervention Questions

Intervention refers to evaluating possible outcomes of a situation with some imposed condition.

University of Michigan - Ann Arbor

The ladder of causation classifies causal reasoning in organisms and machine learning into three levels and corresponding abilities: association (seeing), intervention (doing), and counterfactual reasoning (imagining).

Ladder of Causation: Association, Intervention, and Counterfactuals

Association refers to linking variables together solely based on their perceived outcomes and is not influenced by reasoning. 

Association

Counterfactuals refer to situations that contrast actual events in reality. To answer counterfactual questions, we must go back in time and answer what would have happened if an event happened differently. Assessing counterfactuals is difficult because it is impossible to replicate them in reality. 

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