causalcompass.datasets.confounder.simulate_lorenz_with_confounders

causalcompass.datasets.confounder.simulate_lorenz_with_confounders(p, T, F=10.0, delta_t=0.1, rho=0.5, sd=0.1, burn_in=1000, seed=0)[source]

Generate Lorenz-96 data with hidden confounders.

Parameters:

  • p (int) – Number of variables

  • T (int) – Number of time points

  • F (float, default 10.0) – Forcing parameter

  • delta_t (float, default 0.1) – Time step for ODE solver

  • rho (float, default 0.5) – Probability of confounding between each pair of variables

  • sd (float, default 0.1) – Noise standard deviation

  • burn_in (int, default 1000) – Burn-in period

  • seed (int, default 0) – Random seed

Returns:

(data, GC) — time series array of shape (T, p) and ground-truth causal graph of shape (p, p).

Return type:

tuple