The recent research in the field of counterfactual estimation and explainability has seen significant advancements, particularly in addressing the challenges of unobserved confounders and forward-looking demand behaviors. Innovations in importance sampling methods have enabled more efficient and tractable estimation of counterfactual expressions, transforming variance minimization into a conditional distribution learning problem. This shift has broadened the applicability of these methods to various structural causal models and practical scenarios. Additionally, the development of new distance metrics for counterfactual similarity has allowed for more nuanced dependencies among covariates, enhancing the explainability of machine learning models. The field is also witnessing a push towards building trust in black-box optimization through comprehensive frameworks that provide model-agnostic metrics for transparency and interpretability. Notably, the introduction of novel partial identification strategies for heterogeneous treatment effects, even in the presence of missing covariates, has provided tighter bounds and improved statistical guarantees. These developments collectively underscore a trend towards more robust, interpretable, and actionable counterfactual explanations and causal inference methods.