Class for Linear Gaussian SEMs parametrized by a matrix B representing regression coefficients and a matrix Omega representing correlated errors
Visualize the graph.
:return : dot language representation of the graph.
fit(X, weights=None, tol=1e-06, disp=None)¶
Fit the model to data via (weighted) maximum likelihood estimation
- X – data – a N x M dimensional pandas data frame.
- weights – optional 1d numpy array with weights for each data point (rows with higher weights are given greater importance).
Calculate log-likelihood of the data given the model.
- X – a N x M dimensional data matrix.
a float corresponding to the log-likelihood.
Calculate the total causal effect of a set of treatments A on a set of outcomes Y.
- A – iterable corresponding to variable names that act as treatments.
- Y – iterable corresponding to variable names that act as outcomes.
a float corresponding to the total causal effect.