Exploring Causal Decision Theory Approach with Quantile Regression

Author(s): Shenggang Li

Originally published on Towards AI.

Using AI and Causal Decision Theory to Prioritize Restocking: Balancing Demand, Inventory Risk, and Product Importance

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When facing uncertainties, predictions guide us in selecting the best project from various options. Every decision aims for the best outcome, and Causal Decision Theory (CDT) provides a framework for choosing actions most likely to achieve that.

What if we could apply CDT practically with the support of data and AI?

This post studies a new approach that combines CDT with quantile regression, a machine-learning model that predicts demand at different levels. We use supply chain logistics as an example, where restocking choices are crucial. Should a business prioritize a product in high demand that’s nearly out of stock, or focus on items with stable demand? By forecasting product demands (or sales) — this approach helps weigh risks like stock outs and calculates a utility function to guide restocking decisions.

Although demonstrated here with a supply chain example, this CDT-based model holds promise for other fields where data-driven, practical decisions are important.

Let’s dive into a simple example to see how CDT helps us choose the best stock based on expected returns.

Suppose we’re comparing three stocks: Microsoft (MSFT), Apple (AAPL), and Nvidia (NVDA). We’ll set up a table… Read the full blog for free on Medium.

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Published via Towards AI