ACE

<b>Neural Network Attributions: A Causal Perspective</b><br> Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian<br> Presented at ICML 2019

<pre> <b>Dependencies</b>: scikit-learn (0.19.1) scipy (0.17.0) torch (0.4.0) joblib (0.11) matplotlib (1.5.1) numpy (1.14.5) <b>Usage</b>- MNIST: sh run_mnist_mod.sh MNIST.ipynb Iris: python decision_tree.py train.ipynb ACE.ipynb Synthetic Dataset: toy_dataset.ipynb python evaluate_lstm.py Aircraft: python lstm.py python find_tau.py python aircraft_causal_interventions.py foldername eg. python aircraft_causal_interventions.py "40" python learn_causal_regressors.py learn effect_num_header effect eg. python learn_causal_regressors.py learn 5 LATG python causal_analysis_final.py predict effect foldername start_time eg. python causal_analysis_final.py predict GS "40" 100 </pre>

NASA dataset used in Aircraft code is uploaded at https://drive.google.com/open?id=1rEZ3veRpcKH5OZKAoXuVTyC9oMnn78ra <br> Class-conditional Beta VAE code used in MNIST experiments is adapted from Beta VAE code from https://github.com/1Konny/Beta-VAE <br>

<p> <b>If you use this code, please cite our paper: <br> </b> Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian. "Neural Network Attributions: A Causal Perspective", in International Conference on Machine Learning (ICML), 2019.<br> <b><a href="https://github.com/Piyushi-0/ACE/blob/master/pmlr-v97-chattopadhyay19a.bib">Bibtex</a><br> and consider giving a star to our repository. </b> </p>

<b>References:</b><br> https://github.com/1Konny/Beta-VAE<br> https://c3.nasa.gov/dashlink/projects/85/resources/?type=ds