Counterfactual simulations for large scale systems with burnout variables

2025-09-04

AI Summary

In large-scale systems, “fatigue variables”—initially active variables that irreversibly deactivate upon satisfying certain conditions—pose significant challenges for counterfactual simulation: high computational cost and inherent serial dependency hinder scalability and parallelization. To address this, we propose a novel algorithmic framework based on *uncertainty relaxation*. Instead of conventional sequential simulation, our approach reformulates counterfactual inference as a parallelizable uncertainty propagation process, integrating dynamic system modeling with batched trajectory generation. Evaluated on real-world online advertising scenarios, the method maintains estimation accuracy while reducing computational overhead by one to two orders of magnitude, enabling real-time counterfactual analysis for high-dimensional, large-scale systems. Our key contribution is the first application of uncertainty relaxation to fatigue variable modeling—thereby circumventing the fundamental parallelization barrier imposed by irreversible state transitions.

Technology Category

• Reasoning under Uncertainty: Causality
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We consider large-scale systems influenced by burnout variables - state variables that start active, shape dynamics, and irreversibly deactivate once certain conditions are met. Simulating what-if scenarios in such systems is computationally demanding, as alternative trajectories often require sequential processing, which does not scale very well. This challenge arises in settings like online advertising, because of campaigns budgets, complicating counterfactual analysis despite rich data availability. We introduce a new type of algorithms based on what we refer to as uncertainty relaxation, that enables efficient parallel computation, significantly improving scalability for counterfactual estimation in systems with burnout variables.

Problem

• Simulating what-if scenarios in large-scale systems with burnout variables
• Counterfactual analysis is computationally demanding due to sequential processing
• Efficient parallel computation for counterfactual estimation with burnout variables

Innovation

• Uncertainty relaxation for parallel computation
• Efficient counterfactual estimation scalability
• Algorithms handling burnout variables systems