Long-term Causal Effects Estimation via Latent Surrogates Representation Learning

TL;DR

This paper introduces Laser, a novel method for estimating long-term causal effects using latent surrogate representations, effectively handling the challenge of mixed observed surrogates and proxies in real-world scenarios.

Contribution

The paper proposes a flexible approach utilizing identifiable variational auto-encoder to recover surrogates and estimate long-term causal effects without needing to distinguish surrogates from proxies.

Findings

Laser outperforms existing methods on real-world datasets.

The approach accurately recovers latent surrogates.

It provides unbiased long-term causal effect estimates.

Abstract

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.