Adversarial balancing-based representation learning for causal effect inference with observational data

DOI10.1007/s10618-021-00759-3zbMath1473.68140arXiv1904.13335OpenAlexW3161872343MaRDI QIDQ2036786

Mykola Pechenizkiy, Wouter Duivesteijn, Xin Du, Lei Sun, Alexander G. Nikolaev

Publication date: 30 June 2021

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.13335

zbMATH Keywords

causal inferenceobservational studyrepresentation learninggeneral adversarial networks

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Causal inference from observational studies (62D20)

Related Items

Guest editorial: Special issue on mining for health

Uses Software

• mipmatch
• MatchIt
• TensorFlow
• Adam
• t-SNE
• GitHub
• Wasserstein GAN

Cites Work

• A Mathematical Theory of Communication
• Does matching overcome LaLonde's critique of nonexperimental estimators?
• Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition
• Asymptotic evaluation of certain markov process expectations for large time. IV
• The central role of the propensity score in observational studies for causal effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery
• Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
• Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
• Covariate Balancing Propensity Score
• Causal Inference Using Potential Outcomes
• Random forests
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