Experimental Data on Causal Inference with Unobserved Interacting Factors

Experimental data from two psychology studies investigating how people infer causality when observed causes interact with unobserved factors. The data was authored by Benjamin M. Rottman of Yale University and shared via the paperswithcode platform. The description details experiments where subjects learned to adjust causal inferences based on temporal stability assumptions.

Use Cases

• Modeling human causal learning based on the described experimental paradigm of observed and unobserved cause interactions.
• Benchmarking computational models of causal inference against the described behavioral results.
• Studying the role of temporal stability assumptions in causal reasoning as outlined in the description.

Strengths

• Data originates from two described psychology experiments.
• Authored by a researcher from Yale University.
• Platform tags indicate relevance to cognitive science and causal inference.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Yale University

Collection Method

Experimental data from psychology studies.