Richard Scheines

About

Richard Scheines is the Bess Family Dean of the Marianna Brown Dietrich College of Humanities and Social Sciences at Carnegie Mellon University, where he has served as a professor since 2003 and as Dean since 2014. His research focuses on causal discovery, particularly the problem of learning about causation from statistical evidence. This work is embodied in the TETRAD project, which represents nearly 25 years of collaboration with Clark Glymour, Peter Spirtes, and others, and involves building efficient algorithms for causal discovery that integrate computer science and philosophy. Scheines holds a Ph.D. in History and Philosophy of Science from the University of Pittsburgh, with a thesis on causal models in the social sciences. His areas of specialization include Philosophy of Science (Causation), Artificial Intelligence (Machine Learning), and Educational Computing (Online Courses and Virtual Labs). He has courtesy appointments in the Machine Learning Department and the Human-Computer Interaction Institute at Carnegie Mellon. His professional activities include visiting scholar positions at UC Berkeley, UCLA, and the University of Groningen, and he has been recognized with awards such as the Causality in Statistics Education Award in 2013. His work extends into educational software development, causal inference, and policy advisory roles, contributing significantly to the fields of philosophy of science, machine learning, and educational data mining.

Research topics

• Computer science
• Mathematics
• Econometrics
• Artificial intelligence
• Psychology

Selected publications

• Learning the Structure of Linear Latent Variable Models

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-06-29 · 181 citations

  article

  We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecord…

  PublisherDOI

• Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data

  Figshare · 2018-06-29 · 150 citations

  articleOpen access

  Through their transcript products genes regulate the rates at which an immense variety of transcripts and subsequent proteins occur. Understanding the mechanisms that determine which genes are expressed, and when they are expressed, is one of the keys to genetic manipulation for many purposes, including the development of new treatments for disease. Viewing each gene in a genome as a distinct variable that is either on (expresses) or off (does not express), or more realistically as a continuous…

  OA PDFDOI

• Causality From Probability

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-06-29 · 81 citations

  articleOpen accessSenior author

  Department of Philosophy technical report

  PublisherOA PDFDOI

• Automated Search for Causal Relations: Theory and Practice

  Figshare · 2018-06-29 · 19 citations

  articleOpen access

  Department of Philosophy technical report

  OA PDFDOI

• Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

  Figshare · 2018-06-29 · 14 citations

  articleOpen accessSenior author

  Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from multiple graphical representations of fractions to learning from a single graphical representation. Finding that a simple statistical model did not fit d…

  OA PDFDOI

Frequent coauthors

• Clark Glymour

  82 shared

• Peter Spirtes

  Carnegie Mellon University

  78 shared

• Justin Sytsma

  Victoria University of Wellington

  16 shared

• Édouard Machery

  16 shared

• Jonathan Livengood

  University of Illinois Urbana-Champaign

  16 shared

• Adam Feltz

  University of Oklahoma

  16 shared

• Kevin T. Kelly

  15 shared

• Christopher Meek

  7 shared

Education

• Ph.D., History and Philosophy of Science

  University of Pittsburgh

  1987

Awards & honors

• Causality in Statistics Education Award – 2013
• Best Paper Award – 2013 6th International Workshop on Educat…
• Best Paper Award – 2008 1st International Workshop on Educat…

Similar researchers at Carnegie Mellon University

• Alex John Londonh-39
• Teddy Seidenfeldh-31