Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Scientific vocabulary similarity
Low similarity (8.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: lilyl3
- Language: Python
- Default Branch: master
- Size: 3.23 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
readme.md
Causal Effect Identification
A causal effect measures the impact of an intervention on an outcome; for example, how likely is a customer to buy a car if a company advertises to them? For disjoint sets of treatment variables X and outcome variables Y, the causal effect of X on Y involves computing the probability of y under an intervention on x, commonly denoted $Pr_x(y)$.
The problem of causal effect identifiability asks whether a causal effect can be uniquely determined from a causal graph, an observational distribution $\Pr(\mathbf{V})$ over a subset of variables $\mathbf{V}$, and constraints (e.g. functional dependencies, context-specific independence).
This project implements a tool to systematically incorporate different types of constraints into the identifiability problem by exploiting the Arithmetic Circuits (ACs) [Chen and Darwiche, 2025]. To complement this work, Lily Lin developed a graphical user interace to allow users
to specify the causal graph, causal effects, and additional constraints. Run the GUI by executing vision.py.
Dependancies for vision.py
- Dash
- DashAGGrid
- DashBootstrapComponents
- DashInteractiveGraphviz
- Graphviz
- NetworkX
- Pydot
Run vision.py
vision.py takes no command line inputs, as all are created or loaded through the use of the program.
To run vision.py, it is recommended to create a python virtual envorinment using Python version 3.10, as other version have not been tested and may not have certain packages available. Install to this virtual environment the requirements as laid out in requirements.txt (pip install -r requirements.txt). If on Windows, pip install graphviz 0.20.3 and add its bin folder to your PATH.
To run: python vision.py in the command line of the virtual environment you create.
Saved files
All saved files are stored in the causal_queries_files/query_[#] directory.
Files saved by the GUI:
- BN.net
- csi.json
- query_dict.json
Files saved by the backend:
- latex_formula.txt
- ac.pdf
- simplified_ac.pdf
- ac_with_cut.pdf
Owner
- Login: lilyl3
- Kind: user
- Repositories: 1
- Profile: https://github.com/lilyl3
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: MCSC Capstone Unit Selection GUI
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Seth
family-names: Reis
email: sethreis3@ucla.edu
affiliation: GUI Author
- given-names: Haiying
family-names: Huang
email: hhaiying1998@outlook.com
affiliation: Source Code Author
repository-code: 'https://github.com/SethReis/MSCS_CAPSTONE_CUS_INTERFACE'
license: CC-BY-NC-ND-4.0
commit: c81152f
version: '1.0'
date-released: '2025-03-07'