Probability of Causation with Sample Selection: A Reanalysis of the Impacts of <i>Jóvenes en Acción</i> on Formality

This article identifies the probability of causation when there is sample selection. We show that the probability of causation is partially identified for individuals who are always observed regardless of treatment status and derive sharp bounds under three increasingly restrictive sets of assumptions. The first set imposes an exogenous treatment and a monotone sample selection mechanism. To tighten these bounds, the second set also imposes the monotone treatment response assumption, while the third set additionally imposes a stochastic dominance assumption. Finally, we use experimental data from the Colombian job training program <i>Jóvenes en Acción</i> to empirically illustrate our approach’s usefulness. We find that, among always-employed women, at least 10.2% and at most 13.4% transitioned to the formal labor market because of the program. However, our 90%-confidence region does not reject the null hypothesis that the lower bound is equal to zero.本文针对存在样本选择（sample selection）的情形，开展因果概率（probability of causation）识别研究。我们证明，无论是否接受处理均始终被观测到的个体，其因果概率可被部分识别（partially identified），并基于三组逐步严苛的假设条件推导出精准界（sharp bounds）。第一组假设设定外生处理与单调样本选择机制；为收紧该界的区间，第二组假设额外引入单调处理响应（monotone treatment response）假定，第三组假设则进一步增设随机占优（stochastic dominance）假定。最后，我们借助哥伦比亚就业培训项目《Jóvenes en Acción》的实验数据，实证阐释了本文方法的应用价值。研究发现，在始终处于就业状态的女性群体中，因该项目转入正规劳动力市场的比例至少为10.2%，至多为13.4%。不过，我们的90%置信区域并未拒绝下界为零的原假设。