akshat-causal-toolkit

v0.1.0 suspicious
4.0
Medium Risk

A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell executions, and obfuscation. However, the metadata risk score is elevated due to the unavailability of the corresponding GitHub repository, suggesting potential unreliability or lack of transparency.

  • Elevated metadata risk due to missing repository
  • No direct evidence of malicious activity
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on causal analysis.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package is new and the repository is not found, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_meta_learners.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/YOUR_GITHUB_USERNAME/akshat-causal-toolki
  • Brief PyPI description (739 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 6 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Akshat Gupta" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with akshat-causal-toolkit 
Develop a comprehensive mini-application that utilizes the 'akshat-causal-toolkit' Python package to perform causal inference analysis on synthetic data. The application should be designed to help researchers and data scientists understand the impact of different treatments or interventions on outcomes through various causal inference techniques.

**Project Overview:**
The application will generate synthetic datasets representing a population where each individual has certain covariates and receives a treatment. The goal is to estimate the Average Treatment Effect (ATE) using multiple methods provided by the 'akshat-causal-toolkit'. Additionally, the application should include visualization tools to help interpret the results.

**Core Features:**
1. **Data Generation:** The app should be able to create synthetic datasets with customizable parameters such as sample size, number of covariates, and distribution types.
2. **Treatment Assignment:** Implement a method for assigning treatments to individuals based on their covariates. This could involve propensity score matching or random assignment.
3. **Outcome Simulation:** Simulate outcomes based on the treatment assignment and underlying causal effects.
4. **Causal Inference Methods:** Utilize 'akshat-causal-toolkit' to estimate ATE using at least three different methods (e.g., inverse probability weighting, regression adjustment, and doubly robust estimation).
5. **Visualization:** Include plots that compare estimated ATE values from different methods against the true ATE to evaluate performance.
6. **User Interface:** Design a simple web-based UI using Flask or Streamlit where users can input parameters for data generation and view results.

**Steps to Develop the Application:**
1. Install necessary packages including 'akshat-causal-toolkit', numpy, pandas, matplotlib, seaborn, and any chosen web framework.
2. Create functions for generating synthetic data with specified characteristics.
3. Implement treatment assignment logic and outcome simulation based on the treatment.
4. Integrate 'akshat-causal-toolkit' into your codebase to apply different causal inference methods.
5. Develop a function to visualize and compare results from each method.
6. Build a basic web interface allowing users to customize data generation and view causal inference results.
7. Test the application thoroughly to ensure accuracy and reliability of the results.
8. Document the code and provide clear instructions for running the application.

This project aims to not only showcase the capabilities of 'akshat-causal-toolkit' but also provide a practical tool for understanding and experimenting with causal inference techniques.

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