The field of causal inference and counterfactual analysis is witnessing significant developments, with a focus on improving the reliability and scalability of evaluation methods. Researchers are exploring new approaches to generate synthetic datasets, simulate counterfactual outcomes, and estimate causal effects in complex systems. Notably, there is a growing interest in leveraging deep learning techniques and simulation-based inference to address the challenges of evaluating prescriptive process monitoring methods and estimating causal effects in large-scale systems. These advancements have the potential to enhance the accuracy and efficiency of decision-making in various domains, including process optimization and carbon capture systems. Noteworthy papers include: ProCause, which introduces a generative approach to evaluate prescriptive process monitoring methods, offering improved reliability and flexibility. Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference presents a framework for constructing reliable prediction intervals for individual counterfactual outcomes, achieving tighter intervals while preserving marginal coverage.