Causal Inference Using Linear Time-Varying Filters with Additive Noise
- URL: http://arxiv.org/abs/2012.13025v2
- Date: Fri, 5 Feb 2021 20:56:03 GMT
- Title: Causal Inference Using Linear Time-Varying Filters with Additive Noise
- Authors: Kang Du and Yu Xiang
- Abstract summary: Causal inference using the restricted structural causal model framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms.
We propose to break the symmetry by exploiting the nonstationarity of the data.
Our main theoretical result shows that the causal direction is identifiable in generic cases when cause and effect are connected via a time-varying filter.
- Score: 18.35147325731821
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Causal inference using the restricted structural causal model framework
hinges largely on the asymmetry between cause and effect from the data
generating mechanisms. For linear non-Gaussian noise models and nonlinear
additive noise models, the asymmetry arises from non-Gaussianity or
nonlinearity, respectively. Despite the fact that this methodology can be
adapted to stationary time series, inferring causal relationships from
nonstationary time series remains a challenging task. In this work, we focus on
slowly-varying nonstationary processes and propose to break the symmetry by
exploiting the nonstationarity of the data. Our main theoretical result shows
that the causal direction is identifiable in generic cases when cause and
effect are connected via a time-varying filter. We propose a causal discovery
procedure by leveraging powerful estimates of the bivariate evolutionary
spectra. Both synthetic and real-world data simulations that involve high-order
and non-smooth filters are provided to demonstrate the effectiveness of our
proposed methodology.
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