Description Usage Arguments Value References Examples

The posterior bounds for the Average Causal Effect (ACE) is found based on a transparent reparametrization (see reference below), using a Dirichlet prior. A binary Instrumental Variable (IV) model is assumed here.

1 2 3 |

`n_y0x0z0` |
Number of individuals with Y=0, X=0, Z=0. Alternatively, a vector with elements in the order of the arguments. |

`n_y1x0z0` |
Number of individuals with Y=1, X=0, Z=0. |

`n_y0x1z0` |
Number of individuals with Y=0, X=1, Z=0. |

`n_y1x1z0` |
Number of individuals with Y=1, X=1, Z=0. |

`n_y0x0z1` |
Number of individuals with Y=0, X=0, Z=1. |

`n_y1x0z1` |
Number of individuals with Y=1, X=0, Z=1. |

`n_y0x1z1` |
Number of individuals with Y=0, X=1, Z=1. |

`n_y1x1z1` |
Number of individuals with Y=1, X=1, Z=1. |

`prior` |
Hyperparameters for the Dirichlet prior for p(y, x | z), in the order of the arguments. |

`num.sims` |
Number of Monte Carlo draws from the posterior. |

A data frame with the posterior bounds for the ACE, based only on sampled distributions (from the posterior) that satisfied the IV inequalites.

Richardson, T. S., Evans, R. J., & Robins, J. M. (2011).
Transparent parameterizations of models for potential outcomes.
*Bayesian Statistics, 9, 569-610*.

1 2 3 4 5 6 7 8 9 | ```
ACE_bounds_posterior(158, 14, 0, 0, 52, 12, 23, 78,
prior = c( rep(1, 2), rep(0, 2), rep(1, 4)))
ACE_bounds_posterior(158, 14, 0, 0, 52, 12, 23, 78,
prior = c( rep(1/2, 2), rep(0, 2), rep(1/4, 4)))
## Not run:
ace.bnds.lipid <- ACE_bounds_posterior(158, 14, 0, 0, 52, 12, 23, 78,
prior = c( rep(1, 2), rep(0, 2), rep(1, 4)), num.sims = 2e4)
summary(ace.bnds.lipid)
## End(Not run)
``` |

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