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Deconfounding with Networked Observational Data in a Dynamic Environment

Published: 08 March 2021 Publication History

Abstract

One fundamental problem in causal inference is to learn the individual treatment effects (ITE) -- assessing the causal effects of a certain treatment (e.g., prescription of medicine) on an important outcome (e.g., cure of a disease) for each data instance, but the effectiveness of most existing methods is often limited due to the existence of hidden confounders. Recent studies have shown that the auxiliary relational information among data can be utilized to mitigate the confounding bias. However, these works assume that the observational data and the relations among them are static, while in reality, both of them will continuously evolve over time and we refer such data as time-evolving networked observational data.
In this paper, we make an initial investigation of ITE estimation on such data. The problem remains difficult due to the following challenges: (1) modeling the evolution patterns of time-evolving networked observational data; (2) controlling the hidden confounders with current data and historical information; (3) alleviating the discrepancy between the control group and the treated group. To tackle these challenges, we propose a novel ITE estimation framework Dynamic Networked Observational Data Deconfounder (\mymodel) which aims to learn representations of hidden confounders over time by leveraging both current networked observational data and historical information. Additionally, a novel adversarial learning based representation balancing method is incorporated toward unbiased ITE estimation. Extensive experiments validate the superiority of our framework when measured against state-of-the-art baselines. The implementation can be accessed in \hrefhttps://github.com/jma712/DNDC https://github.com/jma712/DNDC.

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cover image ACM Conferences
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining
March 2021
1192 pages
ISBN:9781450382977
DOI:10.1145/3437963
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 March 2021

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Author Tags

  1. causal inference
  2. dynamic networks
  3. observational data
  4. treatment effect

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  • National Science Foundation (NSF)

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  • (2024)From geometry to causality- ricci curvature and the reliability of causal inference on networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692595(13086-13108)Online publication date: 21-Jul-2024
  • (2024)Doubly robust causal effect estimation under networked interference via targeted learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692319(6457-6485)Online publication date: 21-Jul-2024
  • (2024)Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment MonitoringSensors10.3390/s2402050924:2(509)Online publication date: 14-Jan-2024
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