Ignorability
Ignorability is the causal assumption that treatment assignment is conditionally independent of potential outcomes given specified covariates or confounders.
Causal AI
The ignorability assumption states that a causal variable and its potential outcomes are conditionally independent given specified confounders.
- How it is used
- Supports causal identification by converting intervention queries into estimands based on observed conditional distributions.
- How deep it goes
- Derives backdoor and ETT expressions using conditional independence, d-separation, total probability, chain rule, and consistency.
- Examples
- Uses exposure and in-game purchases for backdoor adjustment, then treatment, outcome, and confounder variables for ETT identification.
- What you can do after
- Evaluate identified intervention effects and effects on the treated through backdoor or SWIG-based estimands.
Causal Inference for Data Science
The ignorability assumption, also called exchangeability, is the condition that potential outcomes are independent of treatment assignment conditional on specified covariates.
- How it is used
- Guides when the potential-outcomes adjustment formula can estimate an average treatment effect.
- How deep it goes
- Defines conditional independence, motivates it through randomization, and connects it to adjustment for the average treatment effect.
- Examples
- Uses randomized treatment assignment and equal age proportions across treated and untreated patients.
- What you can do after
- Apply the adjustment formula when covariates satisfy the ignorability assumption.
Read how Causal Inference for Data Science explains ignorability →
Related concepts
- conditional independenceequivalent to · Two variables are conditionally independent given specified information when their conditional joint distribution factors into the product of their conditional distributions.
- backdoor criterionaddressed by · The backdoor criterion is a graphical condition for identifying a causal effect by requiring a variable set to block every backdoor path while excluding treatment descendants.
- d-separationuses · D-separation is a graphical criterion for determining whether a conditioning set blocks all relevant paths between variables in a graph, implying conditional independence under the graph’s assumptions.
- potential outcomes frameworkused by · The potential outcomes framework defines causal effects by comparing outcomes that would result for a unit under alternative treatments or interventions.
- experimentationproduced by · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
- exchangeabilityequivalent to · Exchangeability is a probabilistic property in which a sequence’s joint distribution is unchanged by permuting its observations.
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