Staged Event Trees for Transparent Treatment Effect Estimation

2025-09-30

AI Summary

This study addresses the lack of transparency and interpretability in estimating average treatment effects (ATE) and conditional average treatment effects (CATE) from observational data. We propose a causal inference framework based on Stage-wise Event Trees (SET), the first to systematically integrate mainstream methods—including propensity score matching, inverse probability weighting, and doubly robust estimation—within a unified graphical structure. Crucially, the framework explicitly maps key causal assumptions (e.g., positivity, unconfoundedness) to nodes in the event tree, enabling visual verification of their validity. Evaluated through extensive simulations and multiple real-world datasets, our approach maintains estimation accuracy while substantially enhancing traceability of causal logic and decision credibility. By rendering implicit assumptions explicit and linking estimation choices to assumption checks, the SET framework provides a rigorous yet interpretable paradigm for analyzing complex causal structures in observational studies.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical settings and policy-making. Beyond the gold-standard estimators from randomized trials, numerous methods have been proposed to estimate treatment effects using observational data. In this paper, we provide a novel characterization of widely used causal inference techniques within the framework of staged event trees, demonstrating their capacity to enhance treatment effect estimation. These models offer a distinct advantage due to their interpretability, making them particularly valuable for practical applications. We implement classical estimators within the framework of staged event trees and illustrate their capabilities through both simulation studies and real-world applications. Furthermore, we showcase how staged event trees explicitly and visually describe when standard causal assumptions, such as positivity, hold, further enhancing their practical utility.

Problem

• Enhancing treatment effect estimation using staged event trees
• Providing interpretable causal inference for observational data
• Visually verifying standard causal assumptions like positivity

Innovation

• Staged event trees enhance treatment effect estimation
• Models provide interpretability for practical applications
• Visual representation verifies standard causal assumptions