Information, Information Processing, Causal Inference & Modeling


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 causal inference. In particular, it focuses on modeling and causal inference with an information-theoretic perspective.

Background: Causal inference or probabilistic causation is the practice and science of identifying the relationship between cause and effect based on observed information. Generally speaking, causal inference deals with inferring that A causes B by looking at information concerning the occurrences of both, while probabilistic causation characterizes causation in terms of probabilities. 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 information-theoretic approaches. In general, we are interested in studying modeling and causality within the info-metrics framework. The main difficulty is not just to infer the probabilities but rather also to identify the causal relationship from the observed information. Thus, 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 (see Pearl's 2009 book on causality).

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

Teddy Seidenfeld, Co-Chair

Committee Members to be announced

Invited Speakers to b announced

Paper Submissions

Details to be announced.


April 13-14, 2018
Carnegie Mellon University



Carniegie Mellon University