使用调查与数字踪迹估计影响：来自多哥随机现金转移支付的证据

中文摘要

我们研究能否利用数字踪迹数据与机器学习的组合来估计项目影响。在多哥开展的一项现金转移支付随机对照试验中，终期调查数据表明，该项目对粮食安全、心理健康和感知经济状况具有正向处理效应。然而，仅依据预测的终期结果进行的影响估计通常不具有统计显著性；这些预测结果由数字踪迹数据和机器学习生成，而该方法确实能够成功预测基线贫困状况。当结合处理后的结果数据与预测值来估计处理效应时，预测的影响与使用调查数据估计的影响相近。

Abstract

We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo , endline survey data indicate positive treatment effects on food security , mental health , and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict baseline poverty) are generally not statistically significant. When post-treatment outcome data are used in conjunction with predictions to estimate treatment effects, predicted impacts are similar to those estimated using surveys.