Understand True Drivers
Correlation doesn’t imply causation. Traditional AI models are good at identifying patterns and correlation within data. However, they struggle to distinguish whether one variable causes another. As a result, traditional AI models may recognize two variables change together, but they cannot reliably determine if one is the direct cause of the other. Howso’s Causal AI goes further by helping you learn where causality exists in your data by identifying more nuanced understanding of cause-and-effect relationships.
Cause-and-Effect
Our data insights are a great place to start your exploration into causality. By leveraging these insights, you can uncover directional indicators that guide where to begin your investigation. After identifying potential causal relationships within your data, you can then move on to quantifying cause-and-effect through advanced causal inference and simulations.
Causality Defined
Causality is designed to offer a deeper and more accurate understanding of the relationships within your data. As a result, organizations can make smarter and more reliable decisions. Howso’s Causal AI solution goes further by including explanations, prescriptors, interrogators, confounders, counterfactuals, what-if analysis, and surprisal. By leveraging these powerful tools, you’ll not only enhance your predictive power but also optimize interventions and improve the robustness, transparency, and ethical considerations of your AI applications.
Make Smarter Decisions
Drive success by understanding the cause-and-effect relationships in your data. Howso’s Causal AI provides the clarity you need.