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Causal Feature Selection.pdf

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Preview of Causal Feature Selection
🔗 Source: proceedings.mlr.press
📊 Size: 812 KB
📄 Pages: 11 pages
⬇️ Downloads: 282

Summary

The estimation of causal treatment effects from observational data requires controlling for all confounders, which can be challenging when dealing with high-dimensional data. To address this, a method is proposed that uses a non-convex joint sparse regularization to select a subset of features that can accurately estimate the treatment effect. This approach can recover the relevant subset with high probability under a linear outcome model and subgaussian covariates, and improves the sample complexity to scale with the cardinality of the sparse subset and log of the dimensionality. Experiments on synthetic and real datasets demonstrate the effectiveness of this method.

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Description

The estimation of causal treatment effects from observational data requires controlling for all confounders, which can be challenging when dealing with...

Technical Information

  • File Format: PDF
  • File Size: 812 KB
  • Pages: 11
  • Language: EN
  • Total Downloads: 282
  • Last Updated: 3 weeks ago

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