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- W3208296444 abstract "Inferring causal effect from observational data has attracted much attention from various domains. Under the potential outcome framework, the estimation of counterfactuals is crucial for the investigation of causal effect at the individual level. Existing representation learning approaches focus on learning one balanced feature space, which ignores certain information predictive to the outcomes. To fully utilize the predictive information, we propose a Subspace learning based Counterfactual Inference (SCI) method to estimate causal effect at the individual level. Different from existing work, SCI learns both a common subspace, which preserves the information across all the treatment groups, and treatment-specific subspaces, which retain the information associated with each specific treatment. Learning from two kinds of subspaces helps SCI obtain better causal effect estimations than state-of-the-art methods, demonstrated by a series of experiments on synthetic and real-world datasets." @default.
- W3208296444 created "2021-11-08" @default.
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- W3208296444 date "2021-10-26" @default.
- W3208296444 modified "2023-10-10" @default.
- W3208296444 title "SCI" @default.
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- W3208296444 doi "https://doi.org/10.1145/3459637.3482175" @default.
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