Information, Causal Models and Model Diagnostics

Where & When

April 14-15, 2018
Carnegie Mellon University

Conference Program

Workshop, Objectives, and Topics

The fundamental concepts of information theory are being used for modeling and inference of problems across most disciplines, such as biology, ecology, economics, finance, physics, political sciences and statistics (for examples, see Fall 2014 conference celebrating the fifth anniversary of the Info-Metrics Institute).

The objective of spring 2018 workshop is to study the interconnection between information, information processing, modeling (or model misspecification and diagnostics) and causal inference. In particular, it focuses on modeling and causal inference with an information-theoretic perspective.

Background: Generally speaking, causal inference deals with inferring that A causes B by looking at information concerning the occurrences of both, while probabilistic causation constrains causation in terms of probabilities and conditional probabilities given interventions. In this workshop we are interested in both. We are interested in studying the modeling framework - including the necessary observed and unobserved required information - that allows causal inference. In particular we are interested in studying modeling and causality within the info-metrics - the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information - framework. Unlike the more 'traditional' inference, causal analysis goes a step further: its aim is to infer not only beliefs or probabilities under static conditions, but also the dynamics of beliefs under changing conditions, such as the changes induced by treatments or external interventions.

This workshop will (i) provide a forum for the dissemination of new research in this area and will (ii) stimulate discussion among research from different disciplines. The topics of interest include both, the more philosophical and logical concepts of causal inference and modeling, and the more applied theory of inferring causality from the observed information. We welcome all topics within the intersection of info-metrics, modeling and causal inference, but we encourage new studies on information or information-theoretic inference in conjunction with causality, model specification (and misspecification). These topics may include, but are not limited to:

  • Causal Inference and Information
  • Probabilistic Causation and Information
  • Nonmonotonic Reasoning, Default Logic and Information-Theoretic Methods
  • Randomized Experiments and Causal Inference
  • Nonrandomized Experiments and Causal Inference
  • Modeling, Model Misspecification and Information
  • Causal Inference in Network Analysis
  • Causal Inference, Instrumental Variables and Information-Theoretic Methods
  • Granger Causality and Transfer Entropy • Counterfactuals, Causality and Policy Analysis in Macroeconomics

Program Committee

  • Richard Scheines, Co-Chair (CMU)
  • Teddy Seidenfeld, Co-Chair (CMU)
  • Amos Golan (American University) Co-Chair

Confirmed Invited Speakers and Discussants

  • Thomas Augustin (Department of Statistics, University of Munich)
  • David Choi (Heinz College of Information Systems and Public Policy, CMU)
  • Gert de Cooman (SYSTeMS Research Group, Ghent University)
  • J. Michael Dunn (Department of Philosophy, Indiana University Bloomington)
  • Frederick Eberhardt (Division of Humanities and Social Sciences, Caltech)
  • Erik Hoel (Department of Biological Sciences, Columbia University)
  • Dominik Janzing (Max Planck Institute for Intelligent Systems)
  • Nicholas (Nick) Kiefer (Cornell)
  • Justin B. Kinney (Cold Spring Harbor Laboratory)
  • David Krakauer (Complexity, Info, Causality; Santa Fe Institute)
  • Sarah E. Marzen (MIT Physics of Living Systems)
  • Alessio Moneta (Institute of Physics, Sant'Anna School of Advanced Studies)
  • Kun Zhang (Department of Philosophy, CMU)