The field of counterfactual decision making and explainability is rapidly advancing, with a focus on developing new methods and techniques for estimating counterfactual outcomes and providing insights into complex decision-making processes. Recent research has introduced new metrics and frameworks for counterfactual decision making, such as the probabilities of potential outcome ranking and the probability of achieving the best potential outcome. Additionally, there has been a surge in the development of explainability methods, including feature importance estimation and counterfactual explanations, which aim to provide transparent and interpretable insights into machine learning models. These advances have the potential to improve decision-making in a variety of applications, including healthcare and finance. Noteworthy papers in this area include: A Bayesian Model for Multi-stage Censoring, which develops a Bayesian model for funnel decision structures and applies it to a dataset of emergency department visits. FLEX: Feature Importance from Layered Counterfactual Explanations, which introduces a framework for converting sets of counterfactuals into feature change frequency scores and evaluates it on two tabular tasks. Synthetic Survival Control: Extending Synthetic Controls for When-If Decision, which proposes a method for estimating counterfactual hazard trajectories in a panel data setting and validates it using a multi-country clinical dataset.