Counterfactual Forecasting of Human Behavior using Generative AI and Causal Graphs

TL;DR

This paper introduces a new framework that combines causal graphs and generative AI to accurately forecast and simulate human behavior under hypothetical scenarios, aiding decision-making.

Contribution

It presents a novel integration of structural causal models with transformer-based generative AI for counterfactual behavior prediction, improving interpretability and accuracy.

Findings

Outperforms traditional forecasting methods on multiple datasets.

Enables realistic simulation of counterfactual user behaviors.

Provides visual causal path explanations for interventions.

Abstract

This study presents a novel framework for counterfactual user behavior forecasting that combines structural causal models with transformer-based generative artificial intelligence. To model fictitious situations, the method creates causal graphs that map the connections between user interactions, adoption metrics, and product features. The framework generates realistic behavioral trajectories under counterfactual conditions by using generative models that are conditioned on causal variables. Tested on datasets from web interactions, mobile applications, and e-commerce, the methodology outperforms conventional forecasting and uplift modeling techniques. Product teams can effectively simulate and assess possible interventions prior to deployment thanks to the framework improved interpretability through causal path visualization.