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Estimating long-term causal effects based on short-term surrogates is asignificant but challenging problem in many real-world applications, e.g.,marketing and medicine. Despite its success in certain domains, most existingmethods estimate causal effects in an idealistic and simplistic way - ignoringthe causal structure among short-term outcomes and treating all of them assurrogates. However, such methods cannot be well applied to real-worldscenarios, in which the partially observed surrogates are mixed with theirproxies among short-term outcomes. To this end, we develop our flexible method,Laser, to estimate long-term causal effects in the more realistic situationthat the surrogates are observed or have observed proxies.Given theindistinguishability between the surrogates and proxies, we utilizeidentifiable variational auto-encoder (iVAE) to recover the whole validsurrogates on all the surrogates candidates without the need of distinguishingthe observed surrogates or the proxies of latent surrogates. With the help ofthe recovered surrogates, we further devise an unbiased estimation of long-termcausal effects. Extensive experimental results on the real-world andsemi-synthetic datasets demonstrate the effectiveness of our proposed method.

Long-term Causal Effects Estimation via Latent Surrogates Representation Learning