ACE_bounds_posterior: Posterior bounds for the Average Causal Effect (ACE). In noncompliance: Causal Inference in the Presence of Treatment Noncompliance Under the Binary Instrumental Variable Model

Description

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.

Usage

 1 2 3 ACE_bounds_posterior(n_y0x0z0, n_y1x0z0 = NA, n_y0x1z0 = NA, n_y1x1z0 = NA, n_y0x0z1 = NA, n_y1x0z1 = NA, n_y0x1z1 = NA, n_y1x1z1 = NA, prior, num.sims = 1000)

Arguments

 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.

Value

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

References

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

Examples

 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)

noncompliance documentation built on May 2, 2019, 2:38 a.m.