Linked causal variational autoencoder for inferring paired spillover effects

Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that depend on the U.S. as their largest export market. In this paper, we aim to infer the causation that results in spillover effects between pairs of entities (or units); we call this effect as paired spillover. To achieve this, we leverage the recent developments in variational inference and deep learning techniques to propose a generative model called Linked Causal Variational Autoencoder (LCVA). Similar to variational au-toencoders (VAE), LCVA incorporates an encoder neural network to learn the latent attributes and a decoder network to reconstruct the inputs. However, unlike VAE, LCVA treats the latent attributes as confounders that are assumed to affect both the treatment and the outcome of units. Specifically, given a pair of units u and u, their individual treatment and outcomes, the encoder network of LCVA samples the confounders by conditioning on the observed covariates of u, the treatments of both u and u and the outcome of u. Once inferred, the latent attributes (or confounders) of u captures the spillover effect of u on u. Using a network of users from job training dataset (LaLonde (1986)) and co-purchase dataset from Amazon e-commerce domain, we show that LCVA is significantly more robust than existing methods in capturing spillover effects.

Original languageEnglish (US)
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages1679-1682
Number of pages4
ISBN (Electronic)9781450360142
DOIs
StatePublished - Oct 17 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: Oct 22 2018Oct 26 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other27th ACM International Conference on Information and Knowledge Management, CIKM 2018
CountryItaly
CityTorino
Period10/22/1810/26/18

Keywords

  • Causal inference
  • Spillover effect
  • Variational autoencoder

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Linked causal variational autoencoder for inferring paired spillover effects'. Together they form a unique fingerprint.

  • Cite this

    Rakesh, V., Guo, R., Moraffah, R., Agarwal, N., & Liu, H. (2018). Linked causal variational autoencoder for inferring paired spillover effects. In N. Paton, S. Candan, H. Wang, J. Allan, R. Agrawal, A. Labrinidis, A. Cuzzocrea, M. Zaki, D. Srivastava, A. Broder, & A. Schuster (Eds.), CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1679-1682). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3269206.3269267