PreprintarXiv licencearXiv
Credible Bounds for Causal Quantities with Continuous Outcomes
Abstract
The problem of partial identification concerns bounding causal quantities that remain unidentifiable given the observed distribution and the causal diagram of the underlying structural causal model (SCM). While bounds have been developed for certain causal quantities with continuous outcomes, they are necessarily univariate and are unable to reflect differing levels of confidence. Building on previous work in partial identification, we propose a simple Bayesian method for deriving probabilistic bounds on a large class of causal quantities with potentially multivariate and/or continuous outcome variables.
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BibTeX
@misc{saporta2026credible,
title = {{Credible Bounds for Causal Quantities with Continuous Outcomes}},
author = {Jason Saporta},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14950},
eprint = {2609.14950},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14950},
}
RIS
TY - UNPB
TI - Credible Bounds for Causal Quantities with Continuous Outcomes
AU - Jason Saporta
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.14950
UR - https://arxiv.org/abs/2609.14950
AB - The problem of partial identification concerns bounding causal quantities that remain unidentifiable given the observed distribution and the causal diagram of the underlying structural causal model (SCM). While bounds have been developed for certain causal quantities with continuous outcomes, they are necessarily univariate and are unable to reflect differing levels of confidence. Building on previous work in partial identification, we propose a simple Bayesian method for deriving probabilistic bounds on a large class of causal quantities with potentially multivariate and/or continuous outcome variables.
ER -