Abstract
We consider situations where data have been collected such that
the sampling depends on the outcome of interest and possibly further covariates,
as for instance in case-control studies. Graphical models represent
assumptions about the conditional independencies among the variables. By
including a node for the sampling indicator, assumptions about sampling
processes can be made explicit. We demonstrate how to read off such graphs
whether consistent estimation of the association between exposure and outcome
is possible. Moreover, we give sufficient graphical conditions for testing
and estimating the causal effect of exposure on outcome. The practical
use is illustrated with a number of examples.
the sampling depends on the outcome of interest and possibly further covariates,
as for instance in case-control studies. Graphical models represent
assumptions about the conditional independencies among the variables. By
including a node for the sampling indicator, assumptions about sampling
processes can be made explicit. We demonstrate how to read off such graphs
whether consistent estimation of the association between exposure and outcome
is possible. Moreover, we give sufficient graphical conditions for testing
and estimating the causal effect of exposure on outcome. The practical
use is illustrated with a number of examples.
| Original language | English |
|---|---|
| Journal | Statistical Science |
| Volume | 25 |
| Issue number | 3 |
| Pages (from-to) | 368-387 |
| Number of pages | 20 |
| ISSN | 0883-4237 |
| DOIs | |
| Publication status | Published - Aug 2010 |