E-commerce demand forecasting is complicated by the effects of latent ranking factors and deliberate price changes that distort observed sales relative to true demand. This paper presents a systematic review of modern approaches to demand modeling from the perspective of integrating probabilistic forecasting, Bayesian uncertainty assessment, and causal analysis. Three key areas are considered: probabilistic deep time series models (DeepAR, Temporal Fusion Transformer, N-BEATS, PatchTST, foundation models Chronos and TimesFM); Bayesian neural networks and ensemble methods for uncertainty assessment; and methods for causal modeling of price effects, including double machine learning and structural causal models. A comparative analysis of methods is conducted based on the criteria for accounting for uncertainty, ranking, and causality. A fundamental gap is identified: the absence of architectures that jointly account for Bayesian uncertainty, position bias, and causal price effects under conditions of unobservable product visibility. A direction for further research is proposed related to the development of integrated Bayesian-causal models with latent ranking variables for reconstructing true demand in e-commerce conditions.