https://github.com/claudiashi57/dragonnet
https://github.com/claudiashi57/dragonnet
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/claudiashi57/dragonnet
- Owner: claudiashi57
- Created: 2019-06-04T03:37:03.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-04-03T07:23:04.000Z (about 3 years ago)
- Last Synced: 2024-08-02T02:17:12.719Z (9 months ago)
- Language: Python
- Size: 5.58 MB
- Stars: 236
- Watchers: 8
- Forks: 50
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - claudiashi57/dragonnet
README
# Introduction
This repository contains software and data for "[Adapting Neural Networks for the Estimation of Treatment Effects](https://arxiv.org/pdf/1906.02120.pdf)".
The paper describes approaches to estimating causal effects from observational data using neural networks. The high-level idea is to modify standard neural net design and training in order to induce a bias towards accurate estimates.
# Requirements and setup
You will need to install tensorflow 1.13, sklearn, numpy 1.15, keras 2.2.4 and, pandas 0.24.1
# Data
1. IHDP
This dataset is based on a randomized experiment investigating the effect of home visits by specialists on future cognitive scores.
It is generated via the npci package [`https://github.com/vdorie/npci`](https://github.com/vdorie/npci) (setting A)
For convenience, we have also uploaded a portion of the simulated data in the dat folder.
This can be used for testing the code.
2. ACIC
ACIC is a collection of semi-synthetic datasets derived from the linked birth and infant death data (LBIDD)
- Here is the full dataset description [`https://www.researchgate.net/publication/11523952_Infant_Mortality_Statistics_from_the_1999_Period_Linked_BirthInfant_Death_Data_Set`](https://www.researchgate.net/publication/11523952_Infant_Mortality_Statistics_from_the_1999_Period_Linked_BirthInfant_Death_Data_Set)
- Here is the GitHub repo associated with the competition [`https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework/blob/master/data/LBIDD/scaling_params.csv`](https://github.com/IBM-HRL-MLHLS/IBM-Causal-Inference-Benchmarking-Framework/blob/master/data/LBIDD/scaling_params.csv)
- For access to the ACIC 2018 competition data: Please see here [`https://www.synapse.org/#!Synapse:syn11294478/wiki/486304`](https://www.synapse.org/#!Synapse:syn11294478/wiki/486304)
# Reproducing neural net training for IHDP experiments
The default setting would let you run Dragonnet, TARNET, and NEDnet under targeted regularization and default mode
You'll run the from `src` code as
`./experiment/run_ihdp.sh`
Before doing this, you'll need to edit `run_ihdp.sh` and change the following:
`data_base_dir= where you stored the data`
`output_base_dir=wherer you want the result to be`
If you only want to run one of the frameworks, delete the rest of the options in `run_ihdp.sh`
# Reproducing neural net training for the ACIC experiment
Same as above except you run the from `src` code as `./experiment/run_acic.sh`
# Computing the ATE
All of the estimators functions are in `semi_parametric_estimation.ate`
To reproduce the table in the paper: i) get the neural net predictions; ii) update the output file location in `ihdp_ate.py` iii) run `ihdp_ate.py`. The `make_table` function should generate the mean absolute error for each framework.
Note: the default code use all the data for prediction and estimation. If you want to get the in-sample or out-sample error: i) change the `train_test_split` criteria in `ihdp_main.py`; ii) rerun the neural net training; iii) run `ihdp_ate.py` with apporiate in-sample data and out-sample data.