Getting Started
This example shows how to generate synthetic data and learn a causal graph from group data alone using gRESIT.
Generating Synthetic Data
We first generate synthetic data using an Erdős–Rényi random graph model. Each group of variables is defined with a specified size and edge density.
from gresit.synthetic_data import GenERData
data_gen = GenERData(
number_of_nodes=10,
group_size=2,
edge_density=0.2,
)
data_dict, _ = data_gen.generate_data(num_samples=1000)
The output data_dict is a dictionary where each key corresponds to a group, and the values are the observed samples. See data for more details on the expected data format.
Fitting a Graph Model
We now fit a group RESIT model using a Multioutcome_MLP as the regressor and HSIC as the independence test.
from gresit.group_resit import GroupResit
from gresit.independence_tests import HSIC
from gresit.torch_models import Multioutcome_MLP
model = GroupResit(
regressor=Multioutcome_MLP(),
test=HSIC,
pruning_method="murgs",
)
learned_dag = model.learn_graph(data_dict=data_dict)
# Show the learned graph:
learned_dag.show()
# or show interactive mode:
model.show_interactive()
Accessing the Learned Graph
The learned adjacency matrix representing the estimated group-level graph and a causal ordering can be accessed via: