By What is a counterfactual in epidemiology?
1. The counterfactual concept is the basis of causal thinking in epidemiology and related fields. It provides the framework for many statistical procedures intended to estimate causal effects and demonstrates the limitations of observational data [10].
What is exchangeability in 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 non exchangeability?
▪ Non-exchangeability arises when. □ the observed outcome in the non-exposed differs from □ what would have occurred in the exposed group in the absence of exposure (the unobserved counterfactual). ▪ It is crucial to account for lack of exchangeability or the results of the study will not be valid.
What is collapsibility in statistics?
4 also establishes a formal connection between confounding and “collapsibility”—a criterion under which a measure of association remains invariant to the omission of certain variables. Definition 6.5.1 (Collapsibility) Let g P(x y)] be any functional23 that measures the association between Y and X in.
What is difference between causation and correlation?
A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events.
What is downward counterfactual thinking?
Downward counterfactual thinking focuses on how the situation could have been worse. In this scenario, a person can make themselves feel better about the outcome because they realize that the situation is not the worst it could be.
What is conditional exchangeability?
Conditional exchangeability essentially means that, even if there are confounding variables that differ between the treatment and control groups that affect the outcome, if we only look at individuals who take a single value for that confounding variable, then the treatment assignment within each strata is “as if” …
What is marginal exchangeability?
Exchangeability is a statement about two variables being independent from each other. The reason we care about conditional independence is that sometimes you may be unwilling to assume that marginal exchangeability Ya=1 ∐ A holds, but you are willing to assume conditional exchangeability Ya=1 ∐ A | L.
What is the average causal effect?
The average causal effect, defined as a contrast of means of counterfactual outcomes, is the most commonly used causal effect. However, the causal effect may also be defined by a contrast of, say, medians, variances, or cdfs of counterfactual outcomes.
What is the meaning of Collapsibility?
1. To fall down or inward suddenly; cave in. 2. To break down suddenly in strength or health and thereby cease to function: a monarchy that collapsed.
Is the risk ratio collapsible?
The risk ratio is collapsible, so adjusting for any variable that is not associated with either the exposure or outcome should not change the magnitude of the risk ratio.
How are the exchangeability assumptions used in epidemiology?
The exchangeability assumptions are well known territory for epidemiologists and biostatisticians. Briefly, to be satisfied, these 2 exchangeability assumptions that require exposed and unexposed subjects, and censored and uncensored subjects have equal distributions of potential outcomes, respectively.
How are identifiability, exchangeability, and confounding related?
Using a simple deterministic model for exposure effects, a logical connection is drawn between the concepts of identifiability, exchangeability, and confounding.
What is the meaning of the word exchangeability?
Exchangeability is meant to capture symmetry in a problem, symmetry in a sense that does not require independence.
What is the problem of bias in epidemiology?
A seemingly unrelated problem in epidemiology is that of confounding: bias in estimation of the effects of an exposure on disease risk, due to inherent differences in risk between exposed and unexposed individuals.