Causalbh

Paper: A data-driven discovery of the causal connection between galaxy and black hole evolution

1. Installation

2. Causal discovery with BGe exact posterior calculation

3. Extensions: PC, FCI and DAG-GFN

4. Data: Black hole mass - galaxy property catalog

5. Reproduce paper plots

6. Cite this work

1. Installation

1.1. clone this repository to your machine

git clone [email protected]:ZehaoJin/causalbh.git

or

git clone https://github.com/ZehaoJin/causalbh.git

1.2. Install dependencies

• We highly recommand install dependencies in a virtual python environment via conda:

  conda create --name causalbh
  conda activate causalbh
  

• Install the GPU version of jax

• To visualize causal graphs, perform analysis around causal graphs, install networkx, pygraphviz, and causallearn

• some basic dependencies such as numpy, scipy, pandas, matplotlib, seaborn, tqdm

• This repository has been tested on:

  python 3.12.2
  jax 0.4.24
  networkx 3.1
  pygraphviz 1.12
  causallearn 0.1.3.8
  

2. Causal discovery with BGe exact posterior calculation

exact posterior is not recommanded for number of nodes $n>7$, see 3. Extensions: PC, FCI and DAG-GFN for cases $n>7$.

2.1. Generate all possible DAGs for n nodes

• Run generate_all_dags.py. Specify the output location and number of nodes $n$ in the script. It will take hours ~ days to run $n$=7.
• Or, use this multiprocessing version generate_all_dags_mp.py. It is also recommanded to verify the generated DAGs are valid using Verify_DAGs.ipynb if generated by the multiprocessing version

2.2 Compute BGe exact posteriors, edge/path marginals with GPU

Follow marginals.ipynb to calculate exact posteriors, and plot edge/path marginals. It will take minutes ~ hours to run for $n$=7.

2.3 A CPU workaround

We here also offer a CPU version to calculate the BGe scores in the case without access to a GPU. After generating all possible DAGs, use cal_bge_cpu.py. This CPU approach is fairly fast for $n\leq5$, but won't be practical for $n>7$.

3. Extensions: PC, FCI and DAG-GFN

3.1. Constriant-Based methods such as PC and FCI

We recommand using the causallearn implementation of PC and FCI alogrithm. Code example can be found here

3.2 DAG-GFlowNet

When the exact posterior approach is computationally infeasible (usually $n>7$), DAG-GFN can be used to approximate the exact posteriors. See DAG-GFN for the implementation of DAG-GFN

4. Data: Black hole mass - galaxy property catalog

• A master catalog that covers 145 SMBHs and more than 100 galaxy properties can be found in SMBH_Data_03_15_24v2.csv
• Sub-catalogs used in this work is sliced from the master catalog. These catalog can be found in this folder. The main result of the paper comes from causal_BH_ell.csv, causal_BH_len.csv, and causal_BH_spr.csv.

5. Reproduce paper plots

• marginals_base+distance.ipynb: main result, and distance as a possible confounder
• marginals_SAM.ipynb: Semi-analytical models
• marginals_stds.ipynb: random sampling from observation errors
• marginals_LOO.ipynb: Leave-One-Out cross validation
• read_exact_posterior.ipynb: Plot causal graphs
• paper_plots_0305.ipynb: Plots related to DAG-GFN

6. Cite this work

If you use this repository or would like to refer the paper, please use the following BibTeX entry:

@article{
}