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P. RIchard Hahn develops novel statistical methods for analyzing data arising from the social sciences: psychology, economics, education, and business. His current focus revolves around causal inference using regression tree models, as well as foundational issues in Bayesian statistics.
- P. Richard Hahn - CV - Arizona State University
P. Richard Hahn. Arizona State University School of...
- P. Richard Hahn - CV - Arizona State University
Articles 1–20. Arizona State University - Cited by 3,561 - causal inference - machine learning - Bayesian statistics.
P. Richard Hahn is an associate professor of statistics and machine learning at Arizona State University. He has expertise in Bayesian methods, causal inference, regression trees, and applications to social, behavioral and health sciences.
Dive into the research topics where Richard Hahn is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
Sep 25, 2018 · A Survey of Learning Causality with Data: Problems and Methods. Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu. This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.
- Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
- arXiv:1809.09337 [cs.AI]
- 2018
- 35 pages, accepted by ACM CSUR
We list three types of data for learning causal effects. First, a standard dataset for learning causal effects includes feature matrix , a vector of treatments and outcomes . We are particularly interested in the causal effect of one variable $t$ (treatment) on another variable $y$ (outcome).
After that, we discuss the connections between learning causality and machine learning. At the end, some open problems are presented to show the great potential of learning causality with data. ACM Reference Format: Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, and Huan Liu. 2010.
