Network-Aware IV Regression with Graph-Fused Lasso

Implements network-aware instrumental variable regression for causal node discovery in high-dimensional settings with graph-structured exposures. Provides IVGL and IVGL-S estimators combining graph-Laplacian penalization with IV-based identification, including correction for invalid instruments via a sisVIVE-style update. Methods are described in Pal and Ghosh (2026) . The 'glmgraph' package, required for the main estimators, is available at the additional repository < https://djghosh1123.r-universe.dev>.

ivgls

ivgls implements network-aware instrumental variable (IV) regression with a graph-fused Lasso penalty for causal variable selection in high-dimensional, graph-structured settings.

Estimators

Function	Graph penalty	Invalid-IV robust
`iv_lasso()`	No	No
`ivgl()`	Yes	No
`ivgl_s()`	Yes	Yes

Installation

glmgraph is required but not on CRAN — install it first:

devtools::install_github("cran/glmgraph")
install.packages("ivgls")

Quick Example

library(ivgls)

set.seed(1)
A    <- make_graph(p = 20, type = "chain")
L    <- get_laplacian(A)
bobj <- generate_beta(A, s2 = 4, signal = 2)
dat  <- generate_data(n = 120, p = 20, q = 60,
                      s_alpha = 5, alpha_strength = 3,
                      beta_true = bobj$beta_true)

fit <- ivgl_s(dat$Y, dat$X, dat$Z, L)
get_mcc(bobj$active_set, which(abs(fit$beta) > 1e-4), p = 20)