A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
Series
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021), Advances in Neural Information Processing Systems
Abstract
Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their ability to yield consistent causal estimates. While they have demonstrated promising results and theory exists on some simple model formulations, we also know that causal effects are not even identifiable in general with latent variables. We investigate this gap between theory and empirical results with analytical considerations and extensive experiments under multiple synthetic and real-world data sets, using the causal effect variational autoencoder (CEVAE) as a case study. While CEVAE seems to work reliably under some simple scenarios, it does not estimate the causal effect correctly with a misspecified latent variable or a complex data distribution, as opposed to its original motivation. Hence, our results show that more attention should be paid to ensuring the correctness of causal estimates with deep latent variable models.
Description
| openaire: EC/H2020/101016775/EU//INTERVENE
Keywords
causal infernce, consistency, deep latent variable model, variational autoencoder, cevae
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Citation
Rissanen , S & Marttinen , P 2021 , A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models . in Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021 . Advances in Neural Information Processing Systems , vol. 6 , Neural Information Processing Systems Foundation , pp. 4207-4217 , Conference on Neural Information Processing Systems , Virtual, Online , 06/12/2021 . < https://papers.nips.cc/paper/2021/hash/21c5bba1dd6aed9ab48c2b34c1a0adde-Abstract.html >