Estimate the total causal effect between two variables (RCD)

Analogous to estimate_total_effect(), but for lingam_rcd() results, which may contain NA entries in the adjacency matrix.

Usage

estimate_total_effect_rcd(
  X,
  rcd_result,
  from_index,
  to_index,
  method = "adaptive_lasso",
  lambda = "BIC",
  init_method = "ols"
)

Arguments

X

Original data (matrix or data.frame)

rcd_result

Return value of lingam_rcd()

from_index

Cause variable (1-based index or variable name)

to_index

Effect variable (1-based index or variable name)

method

Regression method ("ols", "lasso", "adaptive_lasso", "ridge"). Default is adaptive_lasso

lambda

Lambda selection ("lambda.min", "lambda.1se", "AIC", "BIC", "oracle"). Default is BIC

init_method

Method for estimating the initial weights of adaptive LASSO regression ("ols" or "ridge")

Value

Estimated total causal effect, or NA (with a warning) if from_index is part of a suspected latent confounder pair (its parents cannot be identified). Also warns (without altering the estimate) if to_index is an ancestor of from_index according to ancestors_list, since that is inconsistent with a from -> to effect.

Examples

confounded <- generate_rcd_sample(n = 300, seed = 1)
result <- lingam_rcd(confounded$data)

# A well-identified pair returns a numeric estimate
estimate_total_effect_rcd(confounded$data, result, from_index = 6, to_index = 1)
#>      x5 
#> 1.05674