Causal AI & Inference

Share Your Expertise

As AI systems mature, understanding causation becomes crucial for reliable decision-making. Ready Tensor provides a platform for practitioners to showcase their innovations in causal inference, discovery, and modeling. Share your work to advance the field of causality-aware machine learning.

Areas of Interest

Core Approaches:

• Causal inference methods

• Causal discovery algorithms

• Counterfactual analysis

• Treatment effect estimation

• Causal feature selection

• Intervention analysis

Advanced Techniques:

• Structural causal models

• Deep causal learning

• Instrumental variables

• Causal reinforcement learning

• Time-varying causation

• Cross-domain causality

Application Domains:

• Healthcare interventions

• Economic policy analysis

• A/B testing optimization

• Marketing attribution

• Drug discovery

• Social science research

We welcome all innovative approaches in causal AI, from theoretical frameworks to practical implementations in real-world applications.

Types of Publications

From foundational explorations to advanced research, we welcome causal AI publications that advance understanding and practical applications in the field. Share your work to help build a comprehensive knowledge base for the causal AI community.

Research Contributions:

• Original research in causal inference methods

• Surveys of causal discovery techniques

• Comparative analyses of causal estimation approaches

• Novel datasets for causal learning evaluation

• Reproducibility studies of causal methods

Implementation & Applications:

• Real-world causal analysis case studies

• Deployment guides for causal inference tools

• Framework implementations and examples

• Scalable causal discovery solutions

• Competition solution write-ups and analyses

Open Source & Independent Projects:

• Open-source causal inference libraries

• Tools for causal discovery and estimation

• Personal exploration projects

• Side projects implementing novel methods

• Custom implementations of research papers

Educational & Academic:

• Academic research implementations

• Graduate thesis projects

• Interactive causal analysis tutorials

• Course projects and assignments

• Practical guides and learning resources

How to Publish

1. Create a free Ready Tensor account if you don't already have one.

2. To initiate your publication, click 'Start Publication' from the top menu.

3. Follow the intuitive workflow. You can copy/paste content in markdown style, and even upload content from your Jupyter Notebooks. It's fast and easy.

4. Tag your publication with relevant keywords like 'causal-ai', 'causal-inference', or 'counterfactual-analysis' to help others discover your work.

5. Run automated publication assessment to get instant feedback on your documentation quality.

6. Preview your work and click 'Publish' to share it with the community.

7. Note you can edit your documentation even after publication. You do not need to unpublish for editing. All changes are applied automatically.

8. To maximize visibility to your publication, use the social media links to share on platforms like LinkedIn, X, and more.

Maximize Your Chances of Being Featured

To create a publication that stands out, we highly recommend reviewing our comprehensive best practices guide before starting your publication. The guide will help you:

• Choose the most appropriate format for your causal AI project

• Structure your content effectively for your chosen project type

• Understand the technical criteria our judges look for

• Apply proven techniques for enhancing readability and visual appeal

Access our complete best practices guide here: Engage and Inspire: Best Practices for Publishing on Ready Tensor

Pro tip: While writing your publication, keep the guide open in another tab. Even implementing a few of its recommendations can significantly improve your publication's impact and chances of being featured.