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- W4287688251 abstract "Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables given conditional dependencies. In this paper, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within the Pearl's do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables." @default.
- W4287688251 created "2022-07-26" @default.
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- W4287688251 date "2020-08-17" @default.
- W4287688251 modified "2023-09-26" @default.
- W4287688251 title "Estimating Causal Effects with the Neural Autoregressive Density Estimator" @default.
- W4287688251 doi "https://doi.org/10.48550/arxiv.2008.07283" @default.
- W4287688251 hasPublicationYear "2020" @default.
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