Causal Inference Workbench

An interactive platform for deep learning, practical application, and community-driven validation of causal claims.


Explore modules, debug causal claims, and contribute to a growing knowledge base.

Get Started

Interactive Learning Modules

"The first principle is that you must not fool yourself – and you are the easiest person to fool."
- Richard P. Feynman, Cargo Cult Science

"Paper Autopsy" Interface

Select a paper or model output to analyze its causal claims. This interface will visually break down causal chains, highlight assumptions, pinpoint potential biases, and suggest alternative interpretations.

Autopsy Results
Visualized Causal Chain:
Highlighted Assumptions:
    Pinpointed Potential Biases:
      Suggested Alternative Interpretations:
      Decision Support & Next Steps:

      Based on the analysis, consider the following actions to strengthen or refute the causal claim:

        Confidence Score in Causal Claim: N/A

        User Contribution Showcase

        Explore curated examples of causal fallacies and successful causal reasoning submitted by the community.

        Causal Fallacies in Public Discourse

        Successful Causal Reasoning Case Studies

        © Causal Inference Debugger Platform. Inspired by the works of Judea Pearl and Richard Feynman.