The goal of R6causal is to make it easy to work with structural causal models. The R6 class ‘SCM’ contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages ‘igraph’ or ‘qgraph’, 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages ‘causaleffect’ and ‘dosearch’, 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package ‘dosearch’. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
You can install the released version of R6causal from CRAN with:
This is a basic example which shows you how define a structural causal model, plot the graph and simulate data from the model.
library(R6causal)
backdoor <- SCM$new("backdoor",
uflist = list(
uz = function(n) {return(stats::runif(n))},
ux = function(n) {return(stats::runif(n))},
uy = function(n) {return(stats::runif(n))}
),
vflist = list(
z = function(uz) {
return(as.numeric(uz < 0.4))},
x = function(ux, z) {
return(as.numeric(ux < 0.2 + 0.5*z))},
y = function(uy, z, x) {
return(as.numeric(uy < 0.1 + 0.4*z + 0.4*x))}
)
)
#backdoor$plot(vertex.size = 25)
backdoor$simulate(10)
backdoor$simdata
#> uz ux uy z x y
#> 1: 0.95831862 0.39196794 0.321062576 0 0 0
#> 2: 0.43000353 0.02233175 0.617388078 0 1 0
#> 3: 0.48561035 0.91441259 0.001838702 0 0 1
#> 4: 0.06472618 0.82086014 0.390221544 1 0 1
#> 5: 0.36554423 0.51432070 0.203865336 1 1 1
#> 6: 0.75471971 0.57672430 0.391862174 0 0 0
#> 7: 0.75034180 0.85961544 0.652490875 0 0 0
#> 8: 0.46957831 0.20078767 0.608333027 0 0 0
#> 9: 0.09367406 0.29394365 0.488680623 1 1 1
#> 10: 0.92747329 0.84545162 0.230997331 0 0 0