Benchmark mediation estimators across confounding scenarios — gan_mediation

For each scenario in the grid, generates n_iter synthetic datasets with mediator-outcome confounding (via mo_confounding), runs every mediation estimator, and summarises NDE/NIE bias / RMSE / Type I error. This is the mediation analogue of gan_sensitivity().

Usage

gan_mediation_sensitivity(
  trained_gan = NULL,
  conf_grid = c(0.2, 0.5, 0.8),
  coverage_grid = c(0.3, 0.7, 1),
  k_grid = 1,
  mo_confounding = 0.8,
  phi = 0,
  rho_G1 = 0,
  rho_G2 = 0,
  rho_pop = 0,
  lambda_XM = NULL,
  lambda_MY = NULL,
  omega_1 = NULL,
  omega_2 = NULL,
  nc_model = "proxy",
  n_iter = 50,
  n_samples = 500,
  n_features = 20,
  beta_X = 0.1,
  alpha_M = 0.5,
  beta_M = 0.3,
  base_seed = 750,
  n_cores = 1,
  outcome_type = c("continuous", "survival"),
  effect_scale = c("loghr", "rmst"),
  surv_h0 = 0.1,
  surv_event_frac = 0.6,
  surv_censor_rate = NULL
)

Arguments

trained_gan: An iconic_gan (or NULL to use default texture).
conf_grid: Confounding-strength values to sweep. Default c(0.2, 0.5, 0.8).
coverage_grid: Negative-control coverage values in [0,1]. Default c(0.3, 0.7, 1).
k_grid: Numbers of latent confounders to sweep. Default 1.
mo_confounding: Strength of U1 -> M (mediator-outcome confounding). Default 0.80.
phi: Strength of the mediator instrument Gm -> M. 0 = no mediator instrument (five estimators). > 0 = generates Gm and includes the 2-stage MR estimator (IV2SLS2). Default 0.
rho_G1: Correlation of G1 with conf_XM. Default 0.
rho_G2: Correlation of G2 with conf_MY. Default 0.
rho_pop: Shared population structure. Default 0.
lambda_XM: Optional per-path confounder loading vector (X->M path).
lambda_MY: Optional per-path confounder loading vector (M->Y path).
omega_1: Coverage of conf_XM by W1. NULL = use coverage.
omega_2: Coverage of conf_MY by W2. NULL = use coverage.
nc_model: Negative-control model (function or name). Default "proxy".
n_iter: Replicates per scenario. Default 50.
n_samples: Samples per replicate. Default 500.
n_features: Features per replicate. Default 20.
beta_X, alpha_M, beta_M: Causal paths (ground truth). Defaults 0.10 / 0.50 / 0.30.
base_seed: Base RNG seed. Default 750.
n_cores: Parallel workers across replicates. Default 1.
outcome_type: "continuous" (default) or "survival" When survival, the DGP generates time-to-event outcomes and estimation uses the Cox / RMST survival mediation drivers via iconic_estimate().
effect_scale: "loghr" (default) or "rmst". Only used when outcome_type = "survival".
surv_h0: Baseline hazard for survival DGP. See run_single_iteration().
surv_event_frac: Target event fraction for survival DGP.
surv_censor_rate: Censoring rate for survival DGP.

Value

A list with summary (one row per scenario x method, with conf_strength, coverage, k, mo_confounding, phi, true_NDE, true_NIE and NDE/NIE bias/RMSE/Type I columns) and grid.

Details

When phi > 0, a mediator-specific genetic instrument (Gm) is generated and the 2-stage MR estimator (IV2SLS2) is included in the results, enabling point identification of NDE/NIE under M-O confounding.

Examples

sens <- gan_mediation_sensitivity(NULL, conf_grid = 0.8,
  coverage_grid = 0.7, mo_confounding = 0.8,
  n_iter = 2, n_samples = 100, n_features = 5)
head(sens$summary)
#>   conf_strength coverage k mo_confounding phi rho_G1 rho_G2 rho_pop true_NDE
#> 1           0.8      0.7 1            0.8   0      0      0       0      0.1
#> 2           0.8      0.7 1            0.8   0      0      0       0      0.1
#> 3           0.8      0.7 1            0.8   0      0      0       0      0.1
#> 4           0.8      0.7 1            0.8   0      0      0       0      0.1
#> 5           0.8      0.7 1            0.8   0      0      0       0      0.1
#>   true_NIE method     NDE_mean    NDE_bias NDE_pct_bias     NDE_sd  NDE_rmse
#> 1     0.15  UNADJ -0.042487429 -0.14248743   -1.4248743 0.05561129 0.1519409
#> 2     0.15 DIRECT  0.008833256 -0.09116674   -0.9116674 0.11552854 0.1425606
#> 3     0.15   COCA  0.114220263  0.01422026    0.1422026 0.13341666 0.1273665
#> 4     0.15 IV2SLS -0.038700536 -0.13870054   -1.3870054 0.05206206 0.1472320
#> 5     0.15    PGC -0.015543555 -0.11554356   -1.1554356 0.06698824 0.1318673
#>   NDE_mean_se NDE_coverage    NIE_mean   NIE_bias NIE_pct_bias     NIE_sd
#> 1   0.1217711            1  0.71981233  0.5698123     3.798749 0.08766402
#> 2   0.1476969            1  0.32691101  0.1769110     1.179407 0.05025958
#> 3   0.2826143            1 -0.09316795 -0.2431679    -1.621120 0.06778980
#> 4   0.1117838            1  0.41817424  0.2681742     1.787828 0.08425477
#> 5   0.1116669            1  0.30099204  0.1509920     1.006614 0.05776075
#>    NIE_rmse NIE_mean_se NIE_coverage NIE_type1 NDE_type1 n_NDE n_NIE lambda_XM
#> 1 0.5758494  0.11546967          0.0         1         0    10    10          
#> 2 0.1832237  0.11062555          0.9         1         0    10    10          
#> 3 0.2515285  0.17767340          0.8         0         0    10    10          
#> 4 0.2798328  0.09712824          0.3         1         0    10    10          
#> 5 0.1606277  0.09010352          0.7         1         0    10    10          
#>   lambda_MY
#> 1          
#> 2          
#> 3          
#> 4          
#> 5