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Interpretability and causal discovery of the machine learning models to
  predict the production of CBM wells after hydraulic fracturing

Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing

21 December 2022
Chao Min
Guo-quan Wen
Liang Gou
Xiaogang Li
Zhaozhong Yang
    CML
ArXivPDFHTML

Papers citing "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing"

3 / 3 papers shown
Title
Causal Interpretability for Machine Learning -- Problems, Methods and
  Evaluation
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah
Mansooreh Karami
Ruocheng Guo
A. Raglin
Huan Liu
CML
ELM
XAI
45
216
0
09 Mar 2020
A Survey Of Methods For Explaining Black Box Models
A Survey Of Methods For Explaining Black Box Models
Riccardo Guidotti
A. Monreale
Salvatore Ruggieri
Franco Turini
D. Pedreschi
F. Giannotti
XAI
72
3,922
0
06 Feb 2018
Learning Sparse Causal Models is not NP-hard
Learning Sparse Causal Models is not NP-hard
Tom Claassen
Joris Mooij
Tom Heskes
CML
59
119
0
26 Sep 2013
1