Simulation and Application Data for Causal Identification via Non-Linearities

International Studies Quarterly Dataverse hosts replication data for a study on causal identification in observational research. The data, provided by author Christopher Schwarz, likely contains simulation results and application examples related to economic growth and democratization. It was last updated on May 19, 2026.

Use Cases

• Testing novel instrumental variable identification strategies based on non-linearities described in the paper
• Replicating simulations that exploit first-stage non-linearities for causal inference
• Applying provided R code to economic growth or democratization studies as illustrated in the description
• Evaluating the strength of first-stage relationships when using potentially invalid instruments

Strengths

• Data is associated with a peer-reviewed methodology paper published in International Studies Quarterly
• Includes R code to facilitate application in other studies as mentioned in the description
• Last updated on 2026-05-19, suggesting recent maintenance

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

International Studies Quarterly Dataverse

Collection Method

Likely generated through simulations and compiled from application examples as described in the associated paper.

Freshness

Last updated 2026-05-19 17:05:35