Stochastic intervention for causal effect estimation

Tri Dung DUONG, Qian LI, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

6 Citations (Scopus)


Causal inference methods are widely applied in various decision-making domains such as precision medicine, optimal policy and economics. Central to these applications is the treatment effect estimation of intervention strategies. Current estimation methods are mostly restricted to the deterministic treatment, which however, is unable to address the stochastic space treatment policies. Moreover, previous methods can only make binary yes-or-no decisions based on the treatment effect, lacking the capability of providing fine-grained effect estimation degree to explain the process of decision making. In our study, we therefore advance the causal inference research to estimate stochastic intervention effect by devising a new stochastic propensity score and stochastic intervention effect estimator (SIE). Meanwhile, we design a customized genetic algorithm specific to stochastic intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. We provide the theoretical analysis and conduct an empirical study to justify that our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines. Copyright © 2021 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2021 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationDanvers, MA
ISBN (Electronic)9780738133669
Publication statusPublished - 2021


Duong, T. D., Li, Q., & Xu, G. (2021). Stochastic intervention for causal effect estimation. In Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN). IEEE.


  • Stochastic intervention effect
  • Treatment effect estimation
  • Causal inference


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