Computational and Information-Theoretic Questions from Causal Inference (Invited Talk)

Author Leonard J. Schulman

Thumbnail PDF


  • Filesize: 344 kB
  • 1 pages

Document Identifiers

Author Details

Leonard J. Schulman
  • California Institute of Technology, Pasadena, CA, USA

Cite AsGet BibTex

Leonard J. Schulman. Computational and Information-Theoretic Questions from Causal Inference (Invited Talk). In 43rd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 284, p. 3:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Data, for the most part, is used in order to inform potential interventions: whether by individuals (decisions about education or employment), government (public health, environmental regulation, infrastructure investment) or business. The most common data analysis tools are those which identify correlations among variables - think of regression or of clustering. However, some famous paradoxes illustrate the futility of relying on correlations alone without a model for the causal relationships between variables. Historically, causality has been teased apart from correlation through controlled experiments. But for a variety of reasons - cost, ethical constraints, or uniqueness of the system - we must often make do with passive observation alone. A theory based upon directed graphical models has been developed over the past three decades, which in some situations, enables statistically defensible causal inference even in the absence of controlled experiments. Yet "some situations" is rather fewer than one would like. This limitation spurs a range of research questions. In this talk I will describe a couple of causality paradoxes along with how they are captured within the graphical model framework; this will lead naturally toward some of the computational and information-theoretic questions which arise in the theory.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Causal reasoning and diagnostics
  • Computing methodologies → Bayesian network models
  • Causal Inference
  • Bayesian Networks


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail