Counterfactual evaluation of treatment assignment functions with networked observational data

Ruocheng Guo, Jundong Li, Huan Liu

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

4 Scopus citations

Abstract

Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for practitioners. Traditionally, randomized controlled trials (e.g., A/B tests) are performed to evaluate treatment assignment functions. However, they can be time-consuming, expensive, and even unethical in some cases. Therefore, counterfactual evaluation of treatment assignment functions becomes a pressing issue because a massive amount of observational data becomes available in the big data era. Counterfactual evaluation requires controlling the influence of hidden confounders – the unmeasured features that causally influence both treatment assignments and outcomes. However, most of the existing methods rely on the assumption of no hidden confounders. This assumption can be untenable in the context of massive observational data. When such data comes with network information, the later can be potentially useful to correct hidden confounding bias. As such, we first formulate a novel problem, counterfactual evaluation of treatment assignment functions with networked observational data. Then, we investigate the following research questions: How can we utilize network information in counterfactual evaluation? Can network information improve the estimates in counterfactual evaluation? Toward answering these questions, first, we propose a novel framework, Counterfactual Network Evaluator (CONE), which (1) learns partial representations of latent confounders under the supervision of observed treatments and outcomes; and (2) combines them for counterfactual evaluation. Then through extensive experiments, we corroborate the effectiveness of CONE. The results imply that incorporating network information mitigates hidden confounding bias in counterfactual evaluation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
EditorsCarlotta Demeniconi, Nitesh Chawla
PublisherSociety for Industrial and Applied Mathematics Publications
Pages271-279
Number of pages9
ISBN (Electronic)9781611976236
DOIs
StatePublished - 2020
Event2020 SIAM International Conference on Data Mining, SDM 2020 - Cincinnati, United States
Duration: May 7 2020May 9 2020

Publication series

NameProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020

Conference

Conference2020 SIAM International Conference on Data Mining, SDM 2020
CountryUnited States
CityCincinnati
Period5/7/205/9/20

Keywords

  • Causal Inference
  • Counterfactual Evaluation
  • Networked Observational Data

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'Counterfactual evaluation of treatment assignment functions with networked observational data'. Together they form a unique fingerprint.

Cite this