Intervention
An intervention is a deliberate, externally imposed change or control applied to a variable or system to alter or assess its behavior or outcomes.
Causal AI
An intervention is an externally imposed change to a variable or process intended to alter what happens in a causal system.
- How it is used
- Models how an imposed action changes a data-generating process, including changes caused by model-driven decisions.
- How deep it goes
- Defines ideal interventions and works through operators for structural models, graphs, and Pyro, while distinguishing stochastic and non-ideal interventions.
- Examples
- Uses advertising feedback, game engagement experiments, and sampled height outcomes after setting femur length.
- What you can do after
- Apply it via pyro.do to simulate interventional outcomes, and diagnose causal models by comparing predictions with feasible real-world interventions.
Causal Inference for Data Science
An intervention is an externally imposed change to a variable or system intended to reveal or alter causal effects.
- How it is used
- Frames causal questions around treatments or settings that could be changed, rather than outcomes merely predicted from observed data.
- How deep it goes
- Defines graph surgery formally, contrasts observed and intervened distributions, and demonstrates simulation without developing broader identification theory.
- Examples
- Uses kidney-stone treatment, universal treatment assignment, intervened graphs, and R or Python simulations of randomized trials.
- What you can do after
- Apply via R or Python by fixing treatment assignments, simulating randomized assignment, and comparing resulting recovery rates.
Read how Causal Inference for Data Science explains intervention →
Related concepts
- actioncontrasts with · An action is a choice or operation selected or performed by an actor that can affect a system or environment or produce an outcome.
- causal hierarchy theorempart of · The causal hierarchy theorem states that observational, interventional, and counterfactual information are generally distinct levels that cannot be inferred from lower-level information without assumptions corresponding to the higher level.
- counterfactual notationused by · Counterfactual notation represents the value or distribution of a variable under a hypothetical intervention or condition.
- action stepimplemented by · An action step is the stage of counterfactual inference that represents a hypothetical condition as an ideal intervention in a modified causal graph.
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