Causal inference is a statistical analysis that uncovers the independent, actual effect of a certain action on an outcome. In other words, the goal of causal analysis is to explain whether a change in treatment variables actually causes changes to response variables (the outcome).

Traditionally, the standard approach to finding causal effects is Randomized Controlled Trials (aka A/B testing). However, RCTs are often expensive, time-consuming, and potentially unethical.

Lifesight’s Causal Inference analysis, which uses the latest technologies from Causal AI, helps uncover causal effects from observational data and makes it as simple as training a Machine Learning (ML) model without programming. This capability significantly enhances the decision-making process for businesses, making it faster and more cost-effective. By enabling marketers and decision-makers to delve into "How," "Why," and "What if" inquiries, Causal AI offers a deeper and more insightful exploration of their data.