This paper 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 J\'ovenes en Acci\'on 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.

翻译：本文识别存在样本选择情况下的因果概率。我们证明，对于无论处理状态如何均能被始终观测到的个体，其因果概率是部分可识别的，并在三组渐趋严格的假设下推导出尖锐边界。第一组假设施加了外生处理与单调样本选择机制。为收紧边界，第二组假设进一步施加单调处理响应假设，而第三组假设额外施加随机占优假设。最后，我们利用哥伦比亚青年就业培训项目Jóvenes en Acción的实验数据，实证展示了本方法的实用性。研究发现，在持续就业的女性中，至少有10.2%、至多有13.4%因该项目转向正规劳动力市场。然而，我们的90%置信区间未能拒绝下界等于零的原假设。