This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search"

Overview

InvariantAncestrySearch

This repository contains python code necessary to replicated the experiments performed in our paper "Invariant Ancestry Search".

Structure of the repository

The repository is structured in the following manner:

  • In the folder /InvariantAncestrySearch there are two important files:
    • utils.py contains a class DataGenerator which we use for sampling SCMs and data from said sampled SCMs. This, can for instance be done by the sequence
    from InvariantAncestrySearch import DataGenerator
    
    SCM1 = DataGenerator(d = 10, N_interventions = 5, p_conn = 2 / 10, InterventionStrength = 1) # This is an SCM generator
    SCM1.SampleDAG()  # Generates a DAG with d = 10 predictor nodes, 5 interventions and roughly d + 1 edges between the (d + 1)-sized subgraph of (X, Y)
    SCM1.BuildCoefMatrix  # Samples coefficients for the linear assignments -- interventions have strength 1
    data1 = SCM1.MakeData(100)  # Generates 100 samples from SCM1
    
    SCM2 = DataGenerator(d = 6, N_interventions = 1, p_conn = 2 / 6, InterventionStrength = 0.5) # And this is also an SCM generator
    SCM2.SampleDAG()  # Generates a DAG with d = 6 predictor nodes, 1 intervention and roughly d + 1 edges between the (d + 1)-sized subgraph of (X, Y)
    SCM2.BuildCoefMatrix  # Samples coefficients for the linear assignments -- interventions have strength 1
    data2 = SCM2.MakeData(1000)  # Generates 1000 samples from SCM2
    
    • IASfunctions.py includes all relevant functions used in the scripts, e.g., to test for minimal invariance or compute the set of all minimally invariant sets. All functions are documentated.
  • In the folder /simulation_scripts there are scripts to reproduce all experiments performed in the paper. These too documentation inside them. The functions run out-of-the-box, if all necessary libraries are installed and do not need to be run in a certain order.
  • In the folder /output/ there are database files, saved from running the scripts in /simulation_scripts/. These contain the data used to make all figures in the paper and can be opened with the python library shelve.
  • The file requirements.txt contains info on which modules are required to run the code. Note also that an R installation is required as well as the R package dagitty
Owner
Phillip Bredahl Mogensen
I'm Phillip Bredahl Mogensen, a Ph.D. student in statistics at the University of Copenhagen
Phillip Bredahl Mogensen
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