Build robust models with causal discovery
Forecasts based on standard machine learning techniques only extrapolate from historical trends. It is easy for these techniques to build forecasting engines based on historical correlations, as these correlations are found everywhere. However, building models based on these correlative relationships – which tend to be fleeting, continually ebbing and flowing–results in fragile models that don’t perform well when deployed in the real world.
Instead, causaLens uses Causal AI to uncover the true drivers of demand to create predictive causal demand models. These models are robust, autonomously adapting up to three times faster than current state-of-the-art machine learning under regime shifts.