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 StartedInteractive Learning Modules
"The first principle is that you must not fool yourself – and you are the easiest person to fool."
"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.