Time-series representation learning decomposes signals into interpretable factors such as trend and seasonality, and disentangled representation learning assigns these factors to distinct latent dimensions, enhancing interpretability and forecasting accuracy. However, existing methods achieve only statistical disentanglement: an intervention on one generative factor can still influence the latent assigned to the other, since unobserved common causes induce dependence between them. In time-series data, this entanglement is amplified by time-varying confounders, distorting causal effect estimates and degrading counterfactual prediction accuracy. We therefore propose CDTS²—Causal Downstreamer with Causally Disentangled Trend and Seasonality Time-Series Representations. CDTS² combines two complementary mechanisms: (i) dedicated subnetworks separate trend and seasonality, regularized by the Hilbert–Schmidt Independence Criterion (HSIC) to suppress residual dependence between the two latents; and (ii) a causal discovery objective infers a summary causal graph over the input variables and regularizes the shared representation underlying both latents to incorporate causal dependency structure during training. To assess causal disentanglement, we evaluate CDTS² on counterfactual prediction in medical and energy domains. Under severe confounding, CDTS² reduces RMSE by 11.6% and 17.1% over the second-best baselines on the tumor-growth and CityLearn datasets, while preserving the best factual-forecast accuracy. CDTS² also attains the highest Interventional Robustness Score (IRS) and Disentanglement–Completeness–Informativeness (DCI) metrics, indicating that it learns causally disentangled representations that remain robust under interventions.