Counterfactual Bound

Counterfactual bounds represent the range of plausible outcomes if a specific intervention had been applied, addressing the inherent uncertainty in causal inference when not all factors are observable. Current research focuses on developing robust algorithms, often employing expectation-maximization schemes, to estimate these bounds, particularly when dealing with incomplete or biased data from multiple sources, including observational and interventional studies. These methods leverage structural causal models and address limitations of traditional approaches by incorporating heterogeneity and handling selection bias, improving the accuracy and reliability of causal inferences across various applications. The resulting advancements enhance the ability to draw reliable causal conclusions from complex, real-world data.