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Recovering latent confounders from high-dimensional proxy variables

This repository contains the code to replicate the experiments and examples from Recovering latent confounders from high-dimensional proxy variables

requirements

The scripts need R (for the simualtion experiments) and pyhton (for the real-world examples) installed.

The following R packages need to be installed:

  • ggplot2
  • reshape2
  • pbapply
  • optparse
  • fastICA
  • mdatools

The following python libraries need to be available:

  • numpy
  • pandas
  • sklearn
  • jax

reproduce the simulation experiments

To run the simulation experiments and produce the results we provide in the simulations/ folder:

  • generate_data.R a script which generate the synthetic data (run Rscript generate_data.R --help to list all the options).
  • run_methods.R a script which generate the synthetic data (run Rscript run_methods.R --help to list all the options).
  • evaluate.R a script which evaluate the results (run Rscript evaluate.R --help to list all the options).
  • get_plots.R a script to produce the final plots.

The exact simualtion and experiments can be reproduced with the commands in the experiments.sh file.

reproduce GD-PCF method and experiments

To apply the GD-PCF on the simulation experiments and produce the results we provide the gd-pcf/folder:

  • funcs_LNC_lin.py functions that implement the GD-PCF method with linear assumptions
  • funcs_LNC.py function that implement the GD-PCF method without assuming linearity.
  • experiment.py script to apply GD-PCF on one dataset.
  • slurm_script.py calls the experiment.py script for a dataset "job", parametrized by a slurm script that calls it
  • processResults.py functions for gathering the results of GD-PCF for individual datasets which are stored as pickle files and obtaining performance measures.
  • LNC_job.sh slurm script for running GD-PCF on different datasets in parallel