Description Usage Arguments Details Value Author(s) References See Also Examples

`analyze2x2`

performs a causal Bayesian analysis of
a 2 x 2 table in which it is assumed that unmeasured confounding is
present. The binary treatment variable is denoted *X =
0* (control), *1* (treatment); and the binary outcome variable
is denoted *Y = 0* (failure), *1* (success). The
notation and terminology are from Quinn (2008).

1 2 | ```
analyze2x2(C00, C01, C10, C11, a00, a01, a10, a11,
b00, b01, b10, b11, c00, c01, c10, c11, nsamp = 50000)
``` |

`C00` |
The number of observations in |

`C01` |
The number of observations in |

`C10` |
The number of observations in |

`C11` |
The number of observations in |

`a00` |
One of four parameters (with |

`a01` |
One of four parameters (with |

`a10` |
One of four parameters (with |

`a11` |
One of four parameters (with |

`b00` |
One of two parameters (with |

`b01` |
One of two parameters (with |

`b10` |
One of two parameters (with |

`b11` |
One of two parameters (with |

`c00` |
One of two parameters (with |

`c01` |
One of two parameters (with |

`c10` |
One of two parameters (with |

`c11` |
One of two parameters (with |

`nsamp` |
Size of the Monte Carlo sample used to summarize the posterior. |

`analyze2x2`

performs the Bayesian analysis of a 2 x 2
table described in Quinn (2008). `summary`

and `plot`

methods can be used to examine the output.

An object of class `SimpleTable`

.

Kevin M. Quinn

Quinn, Kevin M. 2008. “What Can Be Learned from a Simple Table: Bayesian Inference and Sensitivity Analysis for Causal Effects from 2 x 2 and 2 x 2 x K Tables in the Presence of Unmeasured Confounding.” Working Paper.

`ConfoundingPlot`

, `analyze2x2xK`

, `ElicitPsi`

, `summary.SimpleTable`

, `plot.SimpleTable`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
## Not run:
## Example from Quinn (2008)
## (original data from Oliver and Wolfinger. 1999.
## ``Jury Aversion and Voter Registration.''
## American Political Science Review. 93: 147-152.)
##
## Y=0 Y=1
## X=0 19 143
## X=1 114 473
##
## uniform prior on the potential outcome distributions
S.unif <- analyze2x2(C00=19, C01=143, C10=114, C11=473,
a00=.25, a01=.25, a10=.25, a11=.25,
b00=1, c00=1, b01=1, c01=1,
b10=1, c10=1, b11=1, c11=1)
summary(S.unif)
plot(S.unif)
## a prior belief in an essentially negative monotonic treatment effect
S.mono <- analyze2x2(C00=19, C01=143, C10=114, C11=473,
a00=.25, a01=.25, a10=.25, a11=.25,
b00=0.02, c00=10, b01=25, c01=3,
b10=3, c10=25, b11=10, c11=0.02)
summary(S.mono)
plot(S.mono)
## End(Not run)
``` |

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