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Articles 1–20. Arizona State University - Cited by 3,561 - causal inference - machine learning - Bayesian statistics.
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 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.
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).
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.
Mar 27, 2024 · Richard Hahn is a senior policy analyst at the Niskanen Center and a research scholar at New York University’s Marron Institute of Urban Management, where he served as executive director of the Crime and Justice Program.
