Long-term Causal Effects Estimation via Latent Surrogates Representation
Learning
- URL: http://arxiv.org/abs/2208.04589v3
- Date: Tue, 21 Nov 2023 07:16:51 GMT
- Title: Long-term Causal Effects Estimation via Latent Surrogates Representation
Learning
- Authors: Ruichu Cai, Weilin Chen, Zeqin Yang, Shu Wan, Chen Zheng, Xiaoqing
Yang, Jiecheng Guo
- Abstract summary: Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications.
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.
- Score: 19.4729873433786
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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