New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Prince Harry issues defiant statement as he denies claims he was trying to upstage William by announcing pseudo-royal Canada trip at same time as his brother's five-day tour of Brazil Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes Coffee is something biohacker Bryan Johnson swore off years ago in a bid to improve his health. But it appears a new study has caused the 48-year-old, who claims to be more than a decade younger biologically than his actual age, to rethink his stance on caffeine. In a new video, Johnson highlighted findings from Tulane University in Louisiana that showed coffee drinkers had a 16 percent lower risk of death from any cause and a 31 percent lower risk of cardiovascular disease compared to non-coffee drinkers.

Explaining machine learning (ML) predictions has become crucial as ML models are increasingly deployed in high-stakes domains such as healthcare. While SHapley Additive exPlanations (SHAP) is widely used for model interpretability, it fails to differentiate between causality and correlation, often misattributing feature importance when features are highly correlated. We propose Causal SHAP, a novel framework that integrates causal relationships into feature attribution while preserving many desirable properties of SHAP. By combining the Peter-Clark (PC) algorithm for causal discovery and the Intervention Calculus when the DAG is Absent (IDA) algorithm for causal strength quantification, our approach addresses the weakness of SHAP. Specifically, Causal SHAP reduces attribution scores for features that are merely correlated with the target, as validated through experiments on both synthetic and real-world datasets. This study contributes to the field of Explainable AI (XAI) by providing a practical framework for causal-aware model explanations. Our approach is particularly valuable in domains such as healthcare, where understanding true causal relationships is critical for informed decision-making.