Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite its success in certain domains, most existing methods estimate causal effects in an idealistic and simplistic way - ignoring the causal structure among short-term outcomes and treating all of them as surrogates. However, such methods cannot be well applied to real-world scenarios, in which the partially observed surrogates are mixed with their proxies among short-term outcomes. To this end, we develop our flexible method, Laser, to estimate long-term causal effects in the more realistic situation that the surrogates are observed or have observed proxies.Given the indistinguishability between the surrogates and proxies, we utilize identifiable variational auto-encoder (iVAE) to recover the whole valid surrogates on all the surrogates candidates without the need of distinguishing the observed surrogates or the proxies of latent surrogates. With the help of the recovered surrogates, we further devise an unbiased estimation of long-term causal effects. Extensive experimental results on the real-world and semi-synthetic datasets demonstrate the effectiveness of our proposed method.
翻译:短期替代品估计长期因果效应是许多现实世界应用场景中的重要而具有挑战性的问题,例如营销和医学。尽管某些领域已经取得了成功,大多数现有方法在理想和简化的方式下估计因果效应——忽略短期结果之间的因果结构,并将所有结果都视为替代品。然而,这样的方法不能很好地应用于现实世界的情况,其中部分观察到的替代品与其代理混在短期结果中。为此,我们开发了一种灵活的方法Laser,以在观察到的替代品或具有观察代理的情况下估计长期因果效应。鉴于替代品和代理之间的不可区分性,我们利用可识别的变分自编码器(iVAE)在所有可能的替代品候选中恢复整个有效替代品,而无需区分观察到的替代品或潜在替代品的代理。在恢复替代品的帮助下,我们进一步设计了一种无偏的长期因果效应估计方法。在真实世界和半合成数据集上的广泛实验结果证明了我们提出的方法的有效性。