Deep Structural Causal Models for Tractable Counterfactual Inference

This repository contains the code for the paper

N. Pawlowski⁺, D. C. Castro⁺, B. Glocker. Deep Structural Causal Models for Tractable Counterfactual Inference. Advances in Neural Information Processing Systems. 2020 [NeurIPS Proceedings] [arXiv] [NeurIPS][Poster]

(⁺: joint first authors)

If you use these tools or datasets in your publications, please consider citing the accompanying paper with a BibTeX entry similar to the following:

@inproceedings{pawlowski2020dscm,
    author = {Pawlowski, Nick and Castro, Daniel C. and Glocker, Ben},
    title = {Deep Structural Causal Models for Tractable Counterfactual Inference},
    year = {2020},
    booktitle={Advances in Neural Information Processing Systems},
}

Please refer to the tagged code for the code used for the NeurIPS publication.

Structure

This repository contains code and assets structured as follows:

• deepscm/: contains the code used for running the experiments
  • arch/: model architectures used in experiments
  • datasets/: script for dataset generation and data loading used in experiments
  • distributions/: implementations of useful distributions or transformations
  • experiments/: implementation of experiments
  • morphomnist/: soft link to morphomnist tools in submodules
  • submodules/: git submodules
• assets/
  • data/:
    • morphomnist/: used synthetic morphomnist dataset
    • ukbb/: subset of the ukbb testset that was used for the counterfactuals
  • models/: checkpoints of the trained models

Requirements

We use Python 3.7.2 for all experiments and you will need to install the following packages:

pip install numpy pandas pyro-ppl pytorch-lightning scikit-image scikit-learn scipy seaborn tensorboard torch torchvision

or simply run pip install -r requirements.txt. You will also need to sync the submodule: git submodule update --recursive --init.

Usage

We assume that the code is executed from the root directory of this repository.

Morpho-MNIST

You can recreate the data using the data creation script as:

python -m deepscm.datasets.morphomnist.create_synth_thickness_intensity_data --data-dir /path/to/morphomnist -o /path/to/dataset

where /path/to/morphomnist refers to the directory containing the files from the original MNIST dataset with the original morphometrics from Morpho-MNIST dataset. Alternatively we provide the generated data in data/morphomnist. You can then train the models as:

python -m deepscm.experiments.morphomnist.trainer -e SVIExperiment -m {IndependentVISEM, ConditionalDecoderVISEM, ConditionalVISEM} --data_dir /path/to/data --default_root_dir /path/to/checkpoints --decoder_type fixed_var {--gpus 0}

where IndependentVISEM is the independent model, ConditionalDecoderVISEM is the conditional model and ConditionalVISEM is the full model. The checkpoints are saved in /path/to/checkpoints or the provided checkpoints can be used for testing and plotting:

python -m deepscm.experiments.morphomnist.tester -c /path/to/checkpoint/version_?

where /path/to/checkpoint/version_? refers to the path containing the specific pytorch-lightning run. The notebooks for plotting are situated in deepscm/experiments/plotting/morphomnist.

UKBB

We are unable to share the UKBB dataset. However, if you have access to the UK Biobank or a similar dataset of brain scans, you can then train the models as:

python -m deepscm.experiments.medical.trainer -e SVIExperiment -m ConditionalVISEM --default_root_dir /path/to/checkpoints --downsample 3 --decoder_type fixed_var --train_batch_size 256 {--gpus 0}

The checkpoints are saved in /path/to/checkpoints or the provided checkpoints can be used for testing and plotting:

python -m deepscm.experiments.medical.tester -c /path/to/checkpoint/version_?

The notebooks for plotting are situated in deepscm/experiments/plotting/ukbb.