A robust methodology to causal learning domain invariant feature set from a dataset without utilizing underlying graph structure. Paper for the method: https://arxiv.org/abs/2103.00139
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── Synthetic <- Data that has been generated using R scripts.
│ └── Real <- Data that had been obtained from a real-world scenario.
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├── docs <- A default folder containing the inital results and steps to run the code
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├── references <- Python files containing implemetations of other methods
├── reports
│ ├── figures <- Generated normalized, selective plots used for reporting.
│ ├── supp <- All plots and values for each experiment for each setting.
│ └── Python plots <- Plots for all experimental settings generated in python.
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│
├── requirements.txt <- The requirements file for reproducing the analysis environment
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├── SCTL_experiments.ipynb <- A sample notebook containing experimentation on multiple domain and comparison of SCTL with multiple methods
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├── src <- Source code for use in this project.
│ ├── _pycache_ <- Necessary files for python module
│ │
│ ├── data <- R Scripts to generate data, t-test values and ci-tests.
│ │ ├── data.R
│ │ ├── data_dis_notequal.R
│ │ ├── graph.R
│ │ └── tests and final plots.R
│ │
│ ├── models <- Scripts for raw versions Greedy subset search (GSS) and Exahustive Subset Search (NIPS) version we used
│ │ ├── GSS_NIPS_model.py
│ │ ├── ESS_model_(prediction script).py
│ │ └── ESS_model_(original script).py
│ │
│ ├── all experiements+vizualization <- Complete automated script generating all necessary experiments provided in the paper (values need to be fed for ESS)
│ │ ├── outputs <- supplmentary files and subfiles
│ │ ├── fast_cmim.py
│ │ ├── utils.py
│ │ ├── condense.py <- MAIN FILE - generates plots and runs all experiments
│ │ ├── c45.py
│ │ ├── FCBF_module.py
│ │ ├── test.py
│ │ ├── real_data_experiments.py
│ │ └── real_data_visuals.py
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└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience