Recent advances in treatment effect estimation (TEE) leverage deep neural networks to learn latent representations that balance treatment and control groups. However, despite their empirical success, we know remarkably little about the learned representations themselves—what structure they capture, why they lead to improved performance, and how far we can trust them. Existing evaluations focus almost exclusively on causal outcome metrics such as PEHE or ATE bias, leaving the representational mechanisms largely unexplored. This lack of understanding limits our ability to explain why models perform well, to identify when they fail, and to design more interpretable or trustworthy causal systems. In this work, we present a comprehensive analysis for representation-level evaluation of TEE methods. We benchmark CFR, DRCFR, TEDVAE and modern disentanglement-driven models using a unified suite of quantitative and graphical measures. Quantitatively, we assess AUC(T | Z) for confounding separability, Mutual Information (MI) retention for informativeness, MMD2 and SW1 for balance, CH-ratio for compactness, and Participation Ratio (PR) for effective dimensionality. Graphically, we analyze bias removal through embedding visualizations, kNN-mixing histograms, covariance heatmaps, and outcome-smoothness histograms—providing an interpretable view of latent geometry. Our analysis shows that modern VAE-based disentangling models yield more structured, balanced, and informative representations, which also correlate strongly with causal reliability. We provide a practical way to see inside TEE representations—revealing hidden biases, interpreting latent behavior, and understanding when conventional embedding properties (such as neighborhood preservation) may actually hinder bias mitigation. Overall, our findings emphasize that evaluating how models learn is essential to understanding why they perform well, paving the way toward more interpretable and trustworthy causal inference.