Principal Stratification with Bayesian Additive Regression Trees for Count-Valued Intermediate Variables: Estimating the Effect of Fertility on Women's Employment
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计数型中间变量的主分层与 BART：生育对女性就业的效应
- 研究问题
- 当子女数量为计数型变量、工具有效性随协变量变化且效应异质时，如何估计生育对女性就业的因果效应及群体差异？
- 方法与思路
- 作者将主分层与贝叶斯加性回归树结合的框架扩展到计数型中间变量，灵活建模主层和结果，并定义尊重计数结构的因果目标。
- 材料陈述的贡献
- 摘要称，该方法在所述非线性混杂和异质效应模拟中优于比较方法；三国调查应用显示平均效应和不同女性群体的差异。
- 阅读关注点
- 应核对主层与计数型因果目标的定义、协变量相关工具有效性的假设，以及先验、后验不确定性和模拟比较的设定。
- 阅读边界
- 所据为 arXiv 预印本，不能确认同行评审或期刊接收；灵活建模不自动保证工具有效，摘要未列出完整识别条件或应用区间。
原文摘要
Estimating the causal effect of fertility on women's employment is challenging because fertility and labour-market decisions are jointly determined. Instrumental-variable strategies are widely used, but credible instruments are rare and their validity often depends on covariates. Two-stage least squares, the dominant implementation, does not flexibly accommodate covariate-dependent instrument validity and is poorly suited to count-valued treatment and effect heterogeneity more broadly. We extend an existing framework that combines principal stratification with Bayesian Additive Regression Trees (BART) to settings with count-valued intermediate variables, such as number of children. The approach defines a causal estimand that respects the count structure of the intermediate variable, while BART enables flexible, covariate-dependent modelling of principal strata and outcomes, accommodating covariate-dependent instrument validity and producing estimates of effect heterogeneity. Simulations demonstrate that our approach outperforms conventional and flexible instrumental-variable estimators under nonlinear confounding and heterogeneous treatment effects. Applied to Demographic and Health Survey data from Nigeria, Senegal, and Kenya, the approach suggests a negative average effect in Nigeria but no clear average effect elsewhere, with employment penalties concentrated among younger, less-educated women, heterogeneity that standard approaches would obscure. The approach is available in the R package PrinceBART.