DAG-aware Transformer for Causal Effect Estimation

2024-10-13

arXiv.org

AI Summary

Existing causal inference methods struggle to accurately estimate the Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) under complex causal structures, particularly in the presence of high-dimensional confounding, nonlinear treatment effects, and weak overlap. To address these challenges, we propose DAG-Transformer—a novel paradigm that explicitly incorporates causal Directed Acyclic Graphs (DAGs) into the Transformer architecture for the first time. Our approach integrates DAG-guided attention masking, graph-aware positional encoding, and causal variable embeddings to enable topology-constrained, adaptive feature interaction—eliminating reliance on pre-specified structural assumptions or strong independence conditions. Extensive experiments on multiple synthetic and real-world benchmarks demonstrate that DAG-Transformer consistently outperforms state-of-the-art methods, achieving superior accuracy and robustness under high-dimensional confounding, nonlinear treatment responses, and weak overlap regimes.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Causal inference is a critical task across fields such as healthcare, economics, and the social sciences. While recent advances in machine learning, especially those based on the deep-learning architectures, have shown potential in estimating causal effects, existing approaches often fall short in handling complex causal structures and lack adaptability across various causal scenarios. In this paper, we present a novel transformer-based method for causal inference that overcomes these challenges. The core innovation of our model lies in its integration of causal Directed Acyclic Graphs (DAGs) directly into the attention mechanism, enabling it to accurately model the underlying causal structure. This allows for flexible estimation of both average treatment effects (ATE) and conditional average treatment effects (CATE). Extensive experiments on both synthetic and real-world datasets demonstrate that our approach surpasses existing methods in estimating causal effects across a wide range of scenarios. The flexibility and robustness of our model make it a valuable tool for researchers and practitioners tackling complex causal inference problems.

Problem

• Machine Learning
• Causal Inference
• Treatment Effect

Innovation

• Transformer-based Causal Inference
• Causal Graph Integration
• Treatment Effect Estimation