PreprintCC BY 4.0arXiv
Estimating Causal Bounds from Observational Data With Neural Generative Models
Abstract
Identifying causal bounds from discrete observational data is a fundamental problem in causal inference. While exact analytical bounds exist for certain graphs, this is not generally the case. In this paper, we introduce a framework for estimating such bounds using neural generative models together with a new extrapolation procedure to estimate these bounds more accurately. Across multiple causal inference settings, it produces bounds that are consistently closer to the true bounds than those obtained with existing general neural approaches. We validate the framework on a range of benchmark problems for which exact bounds are available.
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BibTeX
@misc{zhu2026estimating,
title = {{Estimating Causal Bounds from Observational Data With Neural Generative Models}},
author = {Max Zhu and Srivathsan Amruth and Sina Akbari and Qingyuan Zhao},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.25246},
eprint = {2610.25246},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.25246},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Estimating Causal Bounds from Observational Data With Neural Generative Models
AU - Max Zhu
AU - Srivathsan Amruth
AU - Sina Akbari
AU - Qingyuan Zhao
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2610.25246
UR - https://arxiv.org/abs/2610.25246
AB - Identifying causal bounds from discrete observational data is a fundamental problem in causal inference. While exact analytical bounds exist for certain graphs, this is not generally the case. In this paper, we introduce a framework for estimating such bounds using neural generative models together with a new extrapolation procedure to estimate these bounds more accurately. Across multiple causal inference settings, it produces bounds that are consistently closer to the true bounds than those obtained with existing general neural approaches. We validate the framework on a range of benchmark problems for which exact bounds are available.
ER -