Mar 30, 2021 · We apply this framework to inference from observational data under outcome selection bias, assuming access to an auxiliary small dataset ...
We propose an algorithm to estimate causal effects from multiple data sources, where the ATE may be identifiable only in some datasets but not others. The idea ...
Published in Transactions on Machine Learning Research (10/2022). Multi-Source Causal Inference Using. Control Variates under Outcome Selection Bias. Wenshuo ...
Jun 5, 2021 · We apply this framework to inference from observational data under outcome selection bias, assuming access to an auxiliary small dataset ...
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We apply this framework to inference from observational data under an outcome selection bias, assuming access to an auxiliary small dataset from which we ...
This work proposes a general algorithm to estimate causal effects from multiple data sources, where the average treatment effect (ATE) may be identifiable ...
Code for Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias. Authors: Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang ...
Multi-source causal inference using control variates under outcome selection bias. Transactions on Machine Learning Research, 2022. N. Kallus, A. M. Puli ...
Selection bias is induced by preferential selection of units for data analysis, usually governed by unknown factors including treatment, outcome and their con-.
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Abstract—Causal feature selection has attracted much atten- tion in recent years, as the causal features selected imply the causal mechanism related to the ...