Counterfactual outcome
A counterfactual outcome is a hypothetical result that would have occurred under a condition, action, or intervention different from the one observed.
Causal AI
A counterfactual statement is a conditional hypothetical statement whose factual conditions overlap with a hypothetical condition or hypothetical outcome.
- How it is used
- Frames what an observed unit’s outcome might have been under a different condition to inform later decisions.
- How deep it goes
- Develops conditional notation and a question-to-inference workflow, then illustrates model-based inference while noting identification limits.
- Examples
- Uses player purchases, subscriber churn, the Monty Hall problem, and a height-and-femur-length model.
- What you can do after
- Apply it via causal models to formulate and infer alternative outcomes, while assessing whether the available data and assumptions support them.
Causal Inference for Data Science
A counterfactual is a representation of what would have happened to a unit or system under a treatment or condition different from the one actually observed.
- How it is used
- Frames causal effects as comparisons between observed outcomes and outcomes under unreceived treatments or events.
- How deep it goes
- Defines individual and group counterfactuals, then introduces DAG counterfactuals as advanced and mathematically subtle.
- Examples
- Uses patient recovery, movie-release viewing series, and treated-versus-untreated post-event outcomes.
- What you can do after
- Evaluate/understand whether an observed outcome can answer a causal question and which missing alternative outcome must be estimated.
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Delivering Machine Learning Projects: From design to deployment
A counterfactual scenario is a hypothetical situation describing how outcomes might change under an altered condition or input.
- How it is used
- Supports business analysis by letting users explore how altered inputs could change model predictions.
- How deep it goes
- Defines parameterized what-if use but stops short of specifying counterfactual inference algorithms or implementation internals.
- Examples
- Uses altered geographic, product-type, locale, and platform conditions alongside prebuilt scenarios for demand prediction.
- What you can do after
- Apply it via parameterized models to compare alternative predictions and inform business decisions.
Experimentation for Engineers
An outcome or associated information that would occur under a different system state or intervention than the one observed.
- How it is used
- Diagnoses why offline predictions can fail after deploying ranking, recommendation, or trading decisions.
- How deep it goes
- Explains missing-data bias numerically, then connects exploration, policy logging, A/B testing, and simulation limitations.
- Examples
- Uses article ranking, post recommendations, trading strategies, and a three-context viewing-time regression.
- What you can do after
- Apply epsilon-greedy exploration and controlled experiments to collect alternatives and evaluate decision policies.
Read how Experimentation for Engineers explains counterfactual outcome →
Related concepts
- 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.
- binary counterfactual queryincludes · A binary counterfactual query asks what would have happened under a hypothetical binary condition for a binary outcome variable.
- resultkind of · A result is an actual or possible outcome, state, or consequence of an event, action, decision, or process.
- conditional hypotheticalkind of · A conditional hypothetical is a query about an outcome under a specified condition without requiring that the condition replace an observed cause in the same individual.
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