The recent advancements in causal inference and reinforcement learning have significantly pushed the boundaries of what is possible in these fields. A notable trend is the development of methods that handle complex, high-dimensional data, such as images, which were previously underrepresented in causal effect estimation. These new approaches leverage the rich information embedded in such data to achieve more accurate and nuanced causal effect estimates. Additionally, there is a growing focus on addressing the challenges posed by latent confounders and endogenous context variables, with innovative solutions that adapt traditional methods like instrumental variables and constraint-based causal discovery to these more complex scenarios. Another emerging area is the integration of deep learning with traditional causal inference techniques, enhancing the ability to model time-sensitive treatment effects and general interference in networks. Furthermore, the field is seeing advancements in off-policy evaluation, with new estimators that combine the strengths of importance sampling and reward modeling, offering improved performance and robustness. Lastly, the extension of uplift modeling to continuous treatments introduces a new dimension to treatment optimization, allowing for more flexible and resource-efficient decision-making in various real-world applications.