用人口统计数据反推个体生育行为，无需原始个人数据即可预测生育模式。
Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation
- 基于聚合生育率，用神经后验估计反推个体生育偏好与行为参数。
- 在四个国家验证，仅用宏观数据成功预测个体生育时间、家庭规模等结果。
- 生成完整虚拟人生轨迹，降低微观模拟建模的数据依赖，适合政策预测者。
年龄别生育率（ASFRs）是记录生育变迁最全面的数据，但其聚合性掩盖了驱动生育趋势的个体行为机制。为弥合微观-宏观鸿沟，我们提出一种无似然贝叶斯框架，结合可解释的个体级生育过程模拟模型与序列神经后验估计（SNPE）。该框架仅利用ASFRs，成功恢复了当代生育的核心行为参数，包括家庭规模偏好、生育时机选择及避孕失败率。在四个具有不同生育制度的国家队列中验证有效。最令人信服的是，模型仅基于聚合数据，便成功预测了个体层面结果的样本外分布，包括首次性行为年龄、理想家庭规模和生育间隔。由于可生成完整的合成生命历程，该方法显著降低了构建微观模拟模型的数据需求，支持行为明确的人口预测。
原文摘要 · Abstract (English)
Age-specific fertility rates (ASFRs) provide the most extensive record of reproductive change, but their aggregate nature obscures the individual-level behavioral mechanisms that drive fertility trends. To bridge this micro-macro divide, we introduce a likelihood-free Bayesian framework that couples a demographically interpretable, individual-level simulation model of the reproductive process with Sequential Neural Posterior Estimation (SNPE). We show that this framework successfully recovers core behavioral parameters governing contemporary fertility, including preferences for family size, reproductive timing, and contraceptive failure, using only ASFRs. The framework's effectiveness is validated on cohorts from four countries with diverse fertility regimes. Most compellingly, the model, estimated solely on aggregate data, successfully predicts out-of-sample distributions of individual-level outcomes, including age at first sex, desired family size, and birth intervals. Because our framework yields complete synthetic life histories, it significantly reduces the data requirements for building microsimulation models and enables behaviorally explicit demographic forecasts.
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