The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inference

• URL: http://arxiv.org/abs/2511.13018v1
• Date: Mon, 17 Nov 2025 06:16:04 GMT
• Title: The Final-Stage Bottleneck: A Systematic Dissection of the R-Learner for Network Causal Inference
• Authors: Sairam S, Sara Girdhar, Shivam Soni,
• Abstract summary: This paper systematically dissects the R-Learner framework graphs.<n>We provide the first rigorous evidence that the primary driver of performance is the inductive bias of the final-stage CATE estimator.<n>We identify and provide a mechanistic explanation for a subtle, topology-dependent "snuiance bottleneck"
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The R-Learner is a powerful, theoretically-grounded framework for estimating heterogeneous treatment effects, prized for its robustness to nuisance model errors. However, its application to network data, where causal heterogeneity is often graph-dependent, presents a critical challenge to its core assumption of a well-specified final-stage model. In this paper, we conduct a large-scale empirical study to systematically dissect the R-Learner framework on graphs. We provide the first rigorous evidence that the primary driver of performance is the inductive bias of the final-stage CATE estimator, an effect that dominates the choice of nuisance models. Our central finding is the quantification of a catastrophic "representation bottleneck": we prove with overwhelming statistical significance (p < 0.001) that R-Learners with a graph-blind final stage fail completely (MSE > 4.0), even when paired with powerful GNN nuisance models. Conversely, our proposed end-to-end Graph R-Learner succeeds and significantly outperforms a strong, non-DML GNN T-Learner baseline. Furthermore, we identify and provide a mechanistic explanation for a subtle, topology-dependent "nuisance bottleneck," linking it to GNN over-squashing via a targeted "Hub-Periphery Trade-off" analysis. Our findings are validated across diverse synthetic and semi-synthetic benchmarks. We release our code as a reproducible benchmark to facilitate future research on this critical "final-stage bottleneck."

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