ML Anthology

Identifying Direct Causal Effects in Linear Models

Jin Tian
AAAI 2005 pp. 346-353
doi:10.1007/s00894-024-06133-6 /aaai/2005/tian2005aaai-identifying/

Abstract

We utilized the M06-L, HSE06 methods accompanied by the 6-31G* basis sets to perform periodic boundary conditions calculations as implemented in Gaussian 09. The unit cells were sampled employing 100 k-points for geometry optimizations and 2000 k-points for electronic properties The ultrafine grid was employed. Results were visualized employing Gaussview 5.0.1. In addition to this, we performed B3LYP-D3 periodic calculations as implemented in CRYSTAL17.

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Cite

Text

Tian. "Identifying Direct Causal Effects in Linear Models." AAAI Conference on Artificial Intelligence, 2005. doi:10.1007/s00894-024-06133-6

Markdown

[Tian. "Identifying Direct Causal Effects in Linear Models." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/tian2005aaai-identifying/) doi:10.1007/s00894-024-06133-6

BibTeX

@inproceedings{tian2005aaai-identifying,
  title     = {{Identifying Direct Causal Effects in Linear Models}},
  author    = {Tian, Jin},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {346-353},
  doi       = {10.1007/s00894-024-06133-6},
  url       = {https://mlanthology.org/aaai/2005/tian2005aaai-identifying/}
}