Code for paper: Active learning for optimal intervention design in causal models
arXiv link: coming soon...
Download code and run in command line:
pip install -e .
Run on a synthetic instance, e.g.:
python run.py --nnodes 5 --noise_level 1 --DAG_type path --std --a_size 2 --a_target 3 4 --acquisition greedy
Source code folder: ./optint/
More examples given in: ./optint/notebook/test_multigraphs.ipynb
Source code folder: ./perturb-CITE-seq
Notebooks for exploratory data analysis: ./perturb-CITE-seq/preprocess
- download and extract data:
./perturb-CITE-seq/preprocess/screen_sanity_checks.ipynb
- process data:
./perturb-CITE-seq/preprocess/process_data.ipynb
Notebook for running the optimal intervention design task: ./perturb-CITE-seq/test.ipynb
Illustraive figures: made using mac keynotes
Pointers for nonillustrative figures:
./optint/notebook/test_ow.ipynb
: Fig. 3, S2./optint/notebook/test_convergence.ipynb
: Fig. 4./optint/notebook/test_multigraphs.ipynb
: Fig. 5, S4-7./perturb-CITE-seq/preprocess/screen_sanity_checks.ipynb
: Fig. S8, S10, S11A./perturb-CITE-seq/preprocess/process_data.ipynb
: Fig. S9./perturb-CITE-seq/preprocess/test_linearity.ipynb
: Fig. S11C./perturb-CITE-seq/test.ipynb
: Fig. 6, S12-15