Estimate a total causal effect in a VAR-LiNGAM model — estimate_var_total

Estimates the total causal effect from from_index (optionally at lag from_lag) to to_index (at the current time) using the fitted VAR-LiNGAM model. Port of the Python reference estimate_total_effect: the destination variable is regressed on the source variable together with the source's parents (a back-door adjustment), and the source's coefficient is returned.

Usage

estimate_var_total_effect(X, result, from_index, to_index, from_lag = 0)

Arguments

X: original data (matrix or data frame), rows ordered in time
result: a VARLiNGAMResult from lingam_var()
from_index: source variable (1-based index or variable name)
to_index: destination variable (1-based index or variable name)
from_lag: lag of the source variable (0 = current time, default)

Value

the estimated total effect (scalar)

Examples

sample <- generate_varlingam_sample(n = 1000, seed = 42)
model <- lingam_var(sample$data, lags = 1, reg_method = "ols", prune = FALSE)

# total effect of x0 (current) on x2 (current)
estimate_var_total_effect(sample$data, model, from_index = 1, to_index = 3)
#> [1] -0.2558022