Intervention distribution
An intervention distribution is the probability distribution of variables generated by externally setting or changing one or more variables in a causal model.
Causal AI
An intervention distribution is the probability distribution over variables induced by externally setting or changing one or more variables in a causal model.
- How it is used
- Frames deployment changes and simulated experiments as causal interventions rather than ordinary predictions.
- How deep it goes
- Descends from definition to SCM operators, graph surgery, probability kernels, and Pyro sampling, while leaving identification assumptions implicit.
- Examples
- Uses fixed femur length, generated height samples, and an LLM example contrasting conditioned and intervened story distributions.
- What you can do after
- Apply it via Pyro to construct intervention models, generate samples, and compare hypothetical outcomes with observational or counterfactual distributions.
Causal Inference for Data Science
A probability distribution over variables generated under an intervention that fixes a treatment value rather than under natural treatment assignment.
- How it is used
- Frames causal questions about changing treatment, rather than predicting outcomes under natural assignment.
- How deep it goes
- Defines P|do(T = A) and connects it to RCT graphs, while deferring broader estimation methods.
- Examples
- Uses kidney-stone treatment, recovery rates, confounding, and a hypothetical randomized controlled trial.
- What you can do after
- Evaluate/understand outcomes under an imposed treatment and distinguish them from predictions based on observed conditional probabilities.
Read how Causal Inference for Data Science explains intervention distribution →
Related concepts
- conditional probabilitycontrasts with · Conditional probability is the probability of an event or variable value given that specified information, an event, or another variable’s value is known.
- experimentationequivalent to · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
- term frequency-inverse document frequencyused by · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.
- observational joint distributioncontrasts with · An observational joint distribution is the true joint probability distribution of variables as observed without externally imposed interventions.
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