• Ruchi Mangharamani
• Prof. (Dr) Sangeet Vashishtha

Causal AI is revolutionizing healthcare decision-making by providing a framework that integrates interpretability with robust predictive power. This approach addresses the inherent limitations of traditional black-box models, which often lack transparency despite their high performance. By leveraging causal inference, healthcare practitioners can unravel complex relationships between variables, enabling a more nuanced understanding of patient outcomes and treatment effects. This paper explores the synergy between causal AI and policy optimization to bridge the gap between opaque machine learning models and the need for explainable, actionable insights in clinical settings. We present a methodology that combines counterfactual reasoning with advanced optimization techniques to tailor healthcare policies that are both effective and interpretable. The proposed framework not only enhances diagnostic accuracy but also supports personalized treatment strategies, reducing uncertainty in clinical decision-making. Through a series of case studies, the paper demonstrates how causal models can identify critical factors influencing patient health and inform policy adjustments in real-time. Our findings underscore the importance of explainability in fostering trust among clinicians and patients alike. Ultimately, integrating causal AI with policy optimization offers a promising pathway to improve healthcare outcomes, streamline decision-making processes, and pave the way for more ethical and transparent use of artificial intelligence in medicine.

Causal AI, explainable healthcare, decision-making, black-box models, policy optimization, causal inference, personalized medicine

"Causal AI for Explainable Healthcare Decision-Making: Bridging the Gap Between Black-Box Models and Policy Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h663-h672, March-2025, Available :http://www.jetir.org/papers/JETIR2502780.pdf

"Causal AI for Explainable Healthcare Decision-Making: Bridging the Gap Between Black-Box Models and Policy Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph663-h672, March-2025, Available at : http://www.jetir.org/papers/JETIR2502780.pdf