The field of causal inference and treatment effect estimation is rapidly advancing with the development of new methods and techniques. Recent research has focused on improving the accuracy and robustness of treatment effect estimates, particularly in the presence of confounding variables and missing data. Novel approaches, such as contrastive learning and multi-constraint subgroup identification, have shown promise in addressing these challenges. Additionally, there is a growing interest in applying causal inference methods to real-world problems, including personalized medicine and online merchant business diagnosis. Noteworthy papers in this area include CLOC, which proposes a new margin-based contrastive learning method for ordinal classification, and MOSIC, which introduces a model-agnostic framework for optimal subgroup identification under multiple constraints. TSCAN is also noteworthy for its context-aware uplift modeling approach, which adaptively corrects errors estimated by a first-stage model. Overall, these advances have the potential to significantly improve our ability to estimate treatment effects and make informed decisions in a variety of fields.