Corpus ID: 220302057

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

@article{Liao2020ProvablyEN,
  title={Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach},
  author={Luofeng Liao and You-Lin Chen and Zhuoran Yang and Bo Dai and Zhaoran Wang and M. Kolar},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.01290}
}
Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation. We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and… Expand
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