Sequential versus fully conditional specification for tree-based synthesis of health data
Abstract
Disseminating synthetic data in place of original ones is a promising way to enforce privacy while complying with open science principles, especially in the healthcare domain. To keep pace with the rate at which researchers collect such data, there is a need for fast and flexible synthesis procedures. A popular class of such procedures is based on models of conditional distributions induced by decision trees. Such models can be combined into a synthetic distribution following two main approaches: sequential specification and fully conditional specification. However, there is currently no recommendation on which specification to use to optimize the risk-utility tradeoffs. In this work, we use both specifications to synthesize medical datasets by combining tree-based models of conditional distributions. We compare the quality of the resulting synthetic data using several metrics of risk and utility. Our results indicate no general superiority of either specification over the other. Sequential synthesis seems sensitive to the ordering of the variables, while the fully conditional specification involves more hyperparameters and incurs higher computational costs, which may limit its suitability for off-the-shelf synthesis procedures.
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BibTeX
@misc{chapelle2026sequential,
title = {{Sequential versus fully conditional specification for tree-based synthesis of health data}},
author = {Rémy Chapelle and Mohammed Sedki},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35163},
eprint = {2610.35163},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05835658v1},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Sequential versus fully conditional specification for tree-based synthesis of health data
AU - Rémy Chapelle
AU - Mohammed Sedki
PY - 2026
DA - 2026/10/25
PB - HAL
DO - 10.48550/arxiv.2610.35163
UR - https://hal.science/hal-05835658v1
AB - Disseminating synthetic data in place of original ones is a promising way to enforce privacy while complying with open science principles, especially in the healthcare domain. To keep pace with the rate at which researchers collect such data, there is a need for fast and flexible synthesis procedures. A popular class of such procedures is based on models of conditional distributions induced by decision trees. Such models can be combined into a synthetic distribution following two main approaches: sequential specification and fully conditional specification. However, there is currently no recommendation on which specification to use to optimize the risk-utility tradeoffs. In this work, we use both specifications to synthesize medical datasets by combining tree-based models of conditional distributions. We compare the quality of the resulting synthetic data using several metrics of risk and utility. Our results indicate no general superiority of either specification over the other. Sequential synthesis seems sensitive to the ordering of the variables, while the fully conditional specification involves more hyperparameters and incurs higher computational costs, which may limit its suitability for off-the-shelf synthesis procedures.
ER -