Compositional Models for Estimating Causal Effects

2024-06-25

arXiv.org

AI Summary

Existing causal inference methods treat complex structured systems—such as cells, query processors, or households—as monolithic units, relying on fixed features and homogeneous data assumptions, thereby failing to accurately estimate individual treatment effects (ITEs) for heterogeneous components. This work proposes a modular ITE estimation framework based on component composition: it first models potential outcomes at the component level, then hierarchically aggregates them to derive unit-level causal effects. To our knowledge, this is the first causal inference approach enabling systematic generalization—supporting counterfactual predictions for unseen component combinations—while simultaneously improving overlap between treatment and control groups. The method employs a modular neural architecture, component-level potential outcome modeling, and rigorous validation on both synthetic and real-world benchmarks. Experiments demonstrate significant improvements over baselines in three key dimensions: generalization capability, overlap robustness, and ITE estimation accuracy.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Many real-world systems can be represented as sets of interacting components. Examples of such systems include computational systems such as query processors, natural systems such as cells, and social systems such as families. Many approaches have been proposed in traditional (associational) machine learning to model such structured systems, including statistical relational models and graph neural networks. Despite this prior work, existing approaches to estimating causal effects typically treat such systems as single units, represent them with a fixed set of variables and assume a homogeneous data-generating process. We study a compositional approach for estimating individual treatment effects (ITE) in structured systems, where each unit is represented by the composition of multiple heterogeneous components. This approach uses a modular architecture to model potential outcomes at each component and aggregates component-level potential outcomes to obtain the unit-level potential outcomes. We discover novel benefits of the compositional approach in causal inference - systematic generalization to estimate counterfactual outcomes of unseen combinations of components and improved overlap guarantees between treatment and control groups compared to the classical methods for causal effect estimation. We also introduce a set of novel environments for empirically evaluating the compositional approach and demonstrate the effectiveness of our approach using both simulated and real-world data.

Problem

• Complex Systems
• Causal Inference
• Independent Component Analysis

Innovation

• Modular Approach
• Causal Inference
• Unseen Combinations