Causality and Statistical Learning
Andrew Gelman
TL;DR
This paper reviews various approaches and philosophies of causal inference across multiple disciplines, highlighting their differences and commonalities in understanding causality within statistical learning.
Contribution
It provides a comprehensive overview of causal inference methods from sociology, economics, computer science, cognitive science, and statistics.
Findings
Different disciplinary perspectives on causality
Comparison of causal inference approaches
Insights into integrating causal reasoning with statistical learning
Abstract
We review some approaches and philosophies of causal inference coming from sociology, economics, computer science, cognitive science, and statistics
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Qualitative Comparative Analysis Research