SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

2025-02-11

AI Summary

Estimating causal effects on target variables under large-scale variable settings suffers from low efficiency due to the computational burden of full-graph causal discovery. Method: We propose the Sequential Non-Ancestor Pruning (SNAP) framework, which bypasses complete causal graph learning and instead identifies only a targeted subgraph comprising the target variable and its minimal adjustment set. SNAP aligns causal discovery objectives directly with effect estimation requirements, ensuring soundness and completeness in subgraph recovery. It implements a sequential pruning strategy grounded in conditional independence testing and is modular—compatible with standard algorithms (e.g., PC, GES) either as a preprocessing step or standalone procedure. Results: Evaluated on synthetic and real-world datasets, SNAP significantly reduces the number of conditional independence tests and overall runtime, while preserving estimation accuracy for causal effects—demonstrating both computational efficiency and statistical fidelity.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for all variables, but only a small subgraph that includes the targets and their adjustment sets. In this paper, we focus on identifying causal effects between target variables in a computationally and statistically efficient way. This task combines causal discovery and effect estimation, aligning the discovery objective with the effects to be estimated. We show that definite non-ancestors of the targets are unnecessary to learn causal relations between the targets and to identify efficient adjustments sets. We sequentially identify and prune these definite non-ancestors with our Sequential Non-Ancestor Pruning (SNAP) framework, which can be used either as a preprocessing step to standard causal discovery methods, or as a standalone sound and complete causal discovery algorithm. Our results on synthetic and real data show that both approaches substantially reduce the number of independence tests and the computation time without compromising the quality of causal effect estimations.

Problem

• Efficient causal effect estimation
• Pruning non-ancestor variables
• Reducing computational demand

Innovation

• Sequential Non-Ancestor Pruning technique
• Targeted causal effect estimation
• Reduced computational demands