SBCD: Local Constraint-Based Causal Discovery under Selection Bias

Code accompanying the simulation experiments in "Local Constraint Based Causal Discovery under Selection Bias". Use of this source code is governed by a BSD-style license that can be found in the LICENSE file.

Installation

We use R for simulations. The required packages can be installed from CRAN and BioConductor with the following R commands.

install.packages(c("pcalg", "InvariantCausalPrediction", "expm", "PRROC"))
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("RBGL")

Experiments

The experiments are run by calling the Simulate.R script with the appropriate command line arguments.

Rscript Simulate.R [exp] [nseed] [nsamples] [ancestors/patterns]

Here nseed and nsamples are the number of random models and the number of samples gathered in each model. The ancestors/patterns argument indicates the true condition that is used to compare predictions to, and the exp parameter represents with model type are sampled:

• 1: fixed graph
• 2: random small graphs
• 3: random large graphs

In run_simulate.sh we gathered the calls to the simulate.R script that are used to produce the figures in the paper. Results and log files are saved in the results and logs folders respectively. For details we refer to the main paper and appendix.

Citation

The full paper can be found in the Proceedings of Machine Learning Research here, with the following citation.

@inproceedings{versteeg2022local,
  title = {Local Constraint-Based Causal Discovery under Selection Bias},
  author = {Versteeg, Philip and Mooij, Joris and Zhang, Cheng},
  booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning},
  pages = {840--860},
  year = {2022},
  volume = {177},
  series = {Proceedings of Machine Learning Research},
  publisher = {PMLR}
}