chain.causal.multi: Causal estimation on collective outcomes under multiple...

In youjin1207/netchain: Inferring Causal Effects on Collective Outcomes under Interference

Description

This function calculates probability associated with counterfactual collective outcome(s) P(Y(a) = y) when m units are subject to interference and contagion possibly with the presence of multiple confounders. To estimate the magnitude of main effects, two-way interaction effects, or any higher-order interaction effects we use hybrid graphcial models combining features of both log-linear models on undirected graphs (R.matrix) and directed acyclic graphs (DAGs) models used to represent casual relationships.

Usage

chain.causal.multi(targetoutcome = "mean", treatment, inputY, inputA,
  listC, R.matrix, E.matrix, edgeinfo = NULL, n.obs = 1000,
  n.burn = 100, optim.method = "L-BFGS-B")

Arguments

targetoutcome	is a targeted couterfactual outcome of probability is in our interest, having different forms: a vector of length m a vector specifies every element of y. a [q x m] matrix a collection of y_1, y_2, ..., y_q of which we want to derive the probability. an integer the number of 1's in y (0 ≥ & ≤ m). 'mean' when we want derive E(Y(a)) (default).
treatment	a vector of length m representing given treatment assignment a.
inputY	a [n x m] matrix of n independent outcomes for m units.
inputA	a [n x m] matrix of n independent treatment assignments assigned to m units.
listC	is either a matrix, list or NULL: a [n x m] matrix a matrix of n independent confounders for m units under single confounder. a list of [n x m] matrices a collection of n independent confounders for m units under multiple confounders. NULL no confounders.
R.matrix	a [m x m] relational symmetric matrix where R.matrix_ij = 1 indicates Y_i and Y_j are adjacent.
E.matrix	a [m x m] matrix where E.matrix_ij = 1 indicates A_i has a direct causal effect on Y_j. Defaults to diagonal matrix, which indicates no interference.
edgeinfo	a list of matrix specifying additional directed edges (from confounders or treatment to the outcomes) information. Defaults to NULL. first column: "Y" indicates outcomes; "A" indicates treatment; "C" indicates confounders. Under multiple confounders, "C1", "C2", ... indicate each confounder. second column: an index for unit corresponding to the variable in the first column, i=1,2,...m.
n.obs	the number of Gibbs samplers except for burn-in sample.
n.burn	the number of burn-in sample in Gibbs sampling.
optim.method	the method used in optim(). Defaults to "L-BFGS-B".

Value

returns "noconvergence" in case of failure to converence or a list with components :

causalprob	the estimated probability P(Y(a) = y).
n.par	the number of parameters estimated in conditional log-linear model.
par.est	the estimated parameters.

Author(s)

Youjin Lee

Examples

library(netchain)
set.seed(1234)
weight.matrix <- matrix(c(0.5, 1, 0, 1, 0.3, 0.5, 0, 0.5, -0.5), 3, 3)
simobs <- simGibbs(n.unit = 3, n.gibbs = 100, n.sample = 5, 
                  weight.matrix, treat.matrix = 0.5*diag(3), cov.matrix= (-0.3)*diag(3) )
inputY <- simobs$inputY                   
inputA <- simobs$inputA   
inputC <- simobs$inputC 
R.matrix <- ifelse(weight.matrix==0, 0, 1)    
diag(R.matrix) <- 0
edgeinfo <- list(rbind(c("Y", 1), c("C", 1)), rbind(c("Y", 2), c("C", 2)), 
           rbind(c("Y", 3), c("C", 3)))  
# implement a function (take > 10 seconds)
# result <- chain.causal.multi(targetoutcome = "mean",
# treatment <- c(1,0,0), inputY, inputA, listC = inputC, R.matrix, 
# E.matrix <- diag(3), edgeinfo = edgeinfo)

youjin1207/netchain documentation built on May 7, 2019, 9:32 a.m.