crazyoumashu / dpn-sa Goto Github PK

Repository of Deep Propensity Network - Sparse Autoencoder (DPN-SA) to calculate propensity score using sparse autoencoder - Paper under review at Journal of the American Medical Informatics Association.

A deep learning model - deep propensity network using a sparse autoencoder (DPN-SA), for calculating Propensity Score Mathcing(PSN) to tackle the problems of high dimensionality and residual confounding. It uses a sparse autoencoder in place the Propensity dropout module in Deep counterfactual network - Propensity dropout architecture(DCN-PD).

The original paper of DCN-PD can be founed here.

The original implemetation of DCN-PD in pytorch can be found here.

Shantanu Ghosh

Jiang Bian

Yi Guo

Mattia Prosperi

pytorch - 1.3.1
numpy - 1.17.2
pandas - 0.25.1
scikit - 0.21.3
matplotlib: 3.1.1
python - 3.7.4

causal AI, biomedical informatics, deep learning, multitask learning, sparse autoencoder

python 3.7.7

pytorch 1.3.1

Update the DCN Model path

To reproduce the experiments mentioned in the paper for DCN-PD, Logistic regression, Logistic regression Lasso and all the SAE variants of 25-20-10- (greedy and end to end) for both the original and synthetic dataset, type the following command:

python3 main_propensity_dropout.py

After the run, the outputs will be generated in the following location:

IHDP

Consolidated results will be available in textfile in /Details_original.txt and /Details_augmented.txt files.

The details of each run will be avalable in csv files in the following locations:

/MSE/Results_consolidated.csv and /MSE_Augmented/Results_consolidated_NN.csv

© DISL, University of Florida

Licensed under the MIT License