The field of causal discovery and inference is rapidly advancing, with a focus on developing innovative methods to uncover causal relationships in complex systems. Recent developments have highlighted the importance of incorporating causal constraints, handling latent confounders, and improving the robustness of causal effect estimates. Notably, researchers are exploring the use of domain adversarial training, differentiable constraint-based causal discovery, and causal attention mechanisms to enhance the accuracy and reliability of causal inferences. Furthermore, there is a growing interest in applying causal inference techniques to real-world problems, such as sequential recommendation and treatment effect estimation. Some noteworthy papers in this area include: Shylock, which proposes a novel method for causal discovery in multivariate time series, and CausalRec, which integrates causal attention for sequential recommendation. Additionally, the paper on Differentiable Constraint-Based Causal Discovery presents a new approach to causal discovery using gradient-based optimization of conditional independence constraints. The paper on Estimating Treatment Effects in Networks using Domain Adversarial Training also presents a novel method for estimating treatment effects in network settings.