What is ignorability in statistics?

In statistics, ignorability is a feature of an experiment design whereby the method of data collection (and the nature of missing data) do not depend on the missing data.

What is the Unconfoundedness assumption?

The unconfoundedness assumption says loosely that all the variables affecting both the treatment T and the outcome Y are observed (we call them covariates) and can be controlled for.

What is strong ignorability?

When “strong ignorability” holds, Z is admissible, or deconfounding, that is, treatment effects can be estimated without bias using the adjustment estimand, as shown in the derivation of equation (3.54).

What is ignorability assumption?

The ignorability assumption means that if we want to interpret the regression coefficient for treatment as an average causal effect then all the counfounding covariates should be controlled for in the regression model. In a randomized experiments the treatment assignment is ignorable.

What is conditional ignorability?

Given a set of covariates X, conditional ignorability states that treatment asignment D is independent of the potential outcomes that would be realized under treatment Y(1) and control Y(0).

What is Ignorability assumption?

What is conditional Ignorability?

What is exchangeability causal inference?

Causal inference can be conceptualised as a missing data problem in which only one counterfactual outcome is observed for each subject. Exchangeability under design 1 implies that the counterfactual outcomes are missing completely at random.

What is exchangeability in research?

Exchangeability occurs when the unexposed group is a good proxy (i.e., approximation) for the disease experience of the exposed group had they not been exposed.

What does exchangeability mean in the context of the counterfactual model and why is it important?

Exchangeability under design 1 implies that the counterfactual outcomes are missing completely at random. Conditional exchangeability under design 2 implies that the counterfactual outcomes are missing at random (given the variables used to define the randomisation probabilities).

What does exchangeability mean in the context of the counterfactual model?

Formally, exchangeability means that the counterfactual mortality risk under every exposure value a is the same in the exposed and in the unexposed.

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