标题： 使用工具变量因果森林探索印度尼西亚有条件现金转移计划(PKH)对产妇保健利用的异质影响 显示英文标题

标题： Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests

摘要： 本文使用了一种新颖的机器学习方法——工具变量因果森林，探讨了印度尼西亚有条件现金转移计划对孕产妇保健利用的治疗效果异质性。 通过将随机项目分配作为参与该计划的工具变量，我们估计了四个关键结果的条件局部平均治疗效应：良好的辅助分娩、在医疗机构分娩、产前检查和产后检查。 我们发现，根据供给方特征存在显著的治疗效果异质性，尽管在项目开发过程中已经考虑到了供给方的准备情况。 在医生、护士和助产士人数较多的地区，母亲更有可能从该项目中受益，表现为良好辅助分娩率的提高。 我们还发现，根据家庭贫困指标和调查波次的不同，福利差异很大，这反映了项目设计在后期可能发生变化的影响。 2013年对产后检查的影响显示出所有结果中最大的异质性，一些女性在长期获得现金转移后，参加产后检查的可能性降低。 显示英文摘要

摘要： This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia's conditional cash transfer scheme on maternal health care utilisation. Using randomised programme assignment as an instrument for enrollment in the scheme, we estimate conditional local average treatment effects for four key outcomes: good assisted delivery, delivery in a health care facility, pre-natal visits, and post-natal visits. We find significant treatment effect heterogeneity by supply-side characteristics, even though supply-side readiness was taken into account during programme development. Mothers in areas with more doctors, nurses, and delivery assistants were more likely to benefit from the programme, in terms of increased rates of good assisted delivery outcome. We also find large differences in benefits according to indicators of household poverty and survey wave, reflecting the possible impact of changes in programme design in its later years. The impact on post-natal visits in 2013 displayed the largest heterogeneity among all outcomes, with some women less likely to attend post-natal check ups after receiving the cash transfer in the long term.