BCDAG (Bayesian Causal DAG) is a package for Bayesian DAG structure learning and causal effect estimation from observational Gaussian data.
The methodology implemented has been presented in Castelletti, F. & Mascaro, A. (2021). Structural learning and estimation of joint causal effects among network-dependent variables, Statistical Methods & Applications, 1-26
The package can be installed executing the following code:
# install.packages("devtools")
devtools::install_github("alesmascaro/BCDAG")
The workflow of the package consists of two sequential steps: causal
structure learning, performed through the function learn_DAG()
and
causal effect estimation, performed through the function
get_causaleffect()
. For a more detailed description of the inner
mechanisms of these two functions, we refer the reader to the vignettes.
Before using the two main functions of the package, we generate some data:
library(BCDAG)
# Randomly generate a DAG and the DAG-parameters
q = 8
w = 0.2
set.seed(123)
DAG = rDAG(q = q, w = w)
outDL = rDAGWishart(n = 1, DAG = DAG, a = q, U = diag(1, q))
L = outDL$L; D = outDL$D
Sigma = solve(t(L))%*%D%*%solve(L)
n = 200
# Generate observations from a Gaussian DAG-model
X = mvtnorm::rmvnorm(n = n, sigma = Sigma)
We can use the function learn_DAG()
to perform causal structure
learning from the generated observational dataset:
# Run the MCMC
out = learn_DAG(S = 5000, burn = 1000, a = q, U = diag(1,q)/n, data = X, w = w)
Next, we can compute the BMA estimate of the causal effect on a response
variable consequent to a hard intervention on a set of nodes by using
get_causaleffect()
:
# the causal effect on node 1 of an intervention on {3,4}
out |>
get_causaleffect(targets = c(3,4), response = 1)
The three steps here implemented are detailed in the vignettes 1 -
Random data generation from Gaussian
DAG-models,
2 - MCMC scheme for posterior inference of Gaussian DAG models: the
learn_DAG()
function and
3 - Elaborate on the output of learn_DAG()
using get_
functions
- Alessandro Mascaro, Departament d’Economia i Empresa, Universitat Pompeu Fabra, Barcelona, alessandro.mascaro@upf.edu
- Federico Castelletti, Department of Statistical sciences, Università Cattolica del Sacro Cuore, Milan, federico.castelletti@unicatt.it