The field of causal inference is rapidly advancing, with a focus on developing methods that can handle complex causal relationships and heterogeneous treatment effects. Recent research has emphasized the importance of incorporating interventional data and causal graphs to improve the robustness of treatment effect estimates. Additionally, there is a growing interest in developing methods that can handle non-linear and time-varying treatment effects, as well as those that can provide more nuanced and interpretable results. Notable papers in this area include ones that propose novel methods for estimating treatment effects with uncertain causal graphs, and those that develop new approaches for causal domain clustering and sparse causal discovery. Overall, these advances have the potential to improve our understanding of causal relationships and to inform decision-making in a wide range of fields. Noteworthy papers include: Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift, which proposes a new method for learning robust representations of causally-related latent variables. Heterogeneous Causal Learning for Optimizing Aggregated Functions in User Growth, which develops a novel treatment effect optimization methodology to enhance user growth marketing.