Introduction

This repository implements two 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. The exact approach is described in "Adapting Neural Networks for the Estimation of Treatment Effects" (https://arxiv.org/pdf/1906.02120.pdf).

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. We also uploaded a portion of the simulated data in the dat folder.

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
• 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
• For access to the ACIC 2018 competition data: Please see here https://www.synapse.org/#!Synapse:syn11294478/wiki/486304

Reproducing the 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 the ACIC experiment

Same as above except you run the from src code as ./experiment/run_acic.sh