Title:      What Can Be Learned from a Simple Table? Bayesian
Inference and Sensitivity Analysis for Causal Effects from 2x2
and 2x2xK Tables in the Presence of Unmeasured Confounding

Authors:    Kevin Quinn

Entrydate:  2008-09-07 11:45:29

Keywords:   causal inference, bayesian inference, sensitivity
analysis, unmeasured confounding

Abstract:   What, if anything, should one infer about the causal
effect of a binary treatment on a binary outcome from a $2 \times
2$ cross-tabulation of non-experimental data? Many researchers
would answer ``nothing'' because of the likelihood of severe
bias due to the lack of adjustment for key confounding
variables. This paper shows that such a conclusion is unduly
pessimistic. Because the complete data likelihood under
arbitrary patterns of confounding factorizes in a particularly
convenient way, it is possible to parameterize this general
situation with four easily interpretable parameters. Subjective
beliefs regarding these parameters are easily elicited and
subjective statements of uncertainty become possible. This paper
also develops a novel graphical display called the confounding
plot that quickly and efficiently communicates all patterns of
confounding that would leave a particular causal inference
relatively unchanged.

http://polmeth.wustl.edu/retrieve.php?id=827

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