Causal Claims Research Assistant (CClaRA)

1. Exact Cause and Effect Match

   • Example Prompt: "Find papers where fiscal policy causes economic growth."

   • This search will look for papers that contain the specified cause-effect relationship and return exact matches, if available.

2. Cause-only Search

   • Example Prompt: "Find papers with 'job mobility' as a cause."

   • When only a cause is specified, the assistant will return all papers that mention this cause, along with a list of effects associated with it.

3. Effect-only Search

   • Example Prompt: "Find papers with 'earnings growth' as an effect."

   • If only an effect is provided, the assistant will list papers where this effect is documented, identifying the variety of causes related to this effect.

4. Semantic and Related Suggestions

   • Example Prompt: "Find papers where 'government spending' influences 'GDP growth'."

   • If an exact match isn’t found, the assistant will suggest papers with related terms or semantically similar causal claims, helping to broaden the scope when needed.

5. Combining Searches with Broadening Queries

   • Example Prompt: "Find papers where 'education' impacts 'wage growth'."

   • If too few results are found, the assistant may prompt you to expand the query to include related terms, such as including other measures of education or wage outcomes.

1. • "What papers discuss how  monetary policy affects inflation?"

   • "Find studies where technological advancements lead to productivity increases."

   • "Show papers where carbon taxes are linked to emission reductions."

   • "List papers examining the impact of trade openness on economic inequality."

For each search, the assistant will return:

• Paper ID and Title

• Authors and Year of Publication

• The Causal Graph of cause-effect pairs found within the paper

• Journal name, whether it is a "top 5" journal and Citations count (As of November 2024)

If an exact match is unavailable, the assistant will provide a brief explanation on how the returned results relate to your search, such as showing relevant or semantically close pairs.

If you’re interested in the underlying raw data or have specific feedback, please visit our contact form to get in touch. We welcome researchers and authors to provide corrections or enhancements to the causal claims database, ensuring accuracy and utility for the broader research community.