Probabilistic Causality
July 21 - August 1, 2008

Application deadline for scholarship applications: 14 February, 2008
Application deadline for fee-paying applications: 30 May, 2008

Course Director: Miklos Redei, Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, UK

Faculty: Damien Fennell, London School of Economics and Political Science, UK
Gabor Hofer-Szabo, King Sigismund College, Budapest, Hungary
Ferenc Huoranszki, Central European University, Budapest, Hungary
Laszlo E. Szabo, Eötvös University, Budapest, Hungary
Richard E. Neapolitan, Northeastern Illinois University (see one of his videolectures)
Julian Reiss, Erasmus Univeristy

Course Coordinator: Balazs Gyenis, University of Pittsburgh


Probabilistic theories of causation have emerged during the second half of the 20th century with the development of mathematical probability theory and with the great success of probabilistic methods in both the exact and social sciences. One can discern two, complementary trends in this development: motivated by the sciences and relying on their concepts and techniques, abstract, philosophical theories of probabilistic causation were worked out, enriching analytic metaphysics by creating new approaches to the classical problem of causation. Examples of such theories include H. Reichenbach's notion of common cause to explain probabilistic correlations and D. Lewis' theory of counterfactual probabilistic causation. In turn, these philosophically elaborated and technically sharpened new concepts and tools have found their way back to the sciences: interesting new questions about causation in the particular sciences have been raised and new developments in special sciences were triggered. Examples of this latter include the problem of whether quantum correlations can have causal explanations in terms of Reichenbachian common causes, and the theory of causal (Bayes) nets, which are now popular also in computer science. This mutual fertilization of philosophy and sciences is very characteristic of probabilistic causality and makes probabilistic causation a truly interdisciplinary field. The course reviews the recent results in the theory of probabilistic causation, putting the emphasis on the open problems and on the currently debated issues.

The course is divided into five major sections:
  1. Introductory lectures
    A general review of the current status of probabilistic causation is given, and, since notions of probability theory will be extensively used in the subsequent lectures and discussions, one lecture will review briefly the relevant concepts of both classical and non-classical (quantum) probability theory.

  2. The Common Cause Principle
    In this block Reichenbach's classical notion of common cause and the related Common Cause Principle is recalled and analyzed. The problem of causal (in)completeness and common cause completability of classical and quantum probability spaces is defined, and results on common cause (in)completeness and common cause completability are presented and analyzed. Generalizations of the notion of common cause to common cause systems will be given and, after proving existence theorems about Reichenbachian Common Cause systems, properties of these common cause systems will be investigated. Causal nets and the causal Markov condition used in Bayes nets are generalizations of the Common Cause Principle. The basic definitions of and the recent debates about the causal Markov condition will be reviewed and discussed.

  3. Causal explanations in physics
    EPR correlations predicted by quantum mechanics (and observed in Nature) are a special challenge for the Common Cause Principle because to explain these correlations in terms of common causes, the common causes need to satisfy additional locality conditions that express the no-action-at-a-distance principle of relativistic physics. The lectures in this section review the possible formulations of the locality conditions and the new No-go theorems that have been recently obtained, which indicate that EPR correlations cannot be explained by local Reichenbachian common causes. Local (relativistic) quantum field theory also predicts correlations between causally disjoint (spacelike) entities; hence the status of the Common Cause Principle arises in quantum field theory as well, this (largely) open problem will be analyzed.

  4. Probabilistic causality in economics
    There is a long tradition of concepts of causality in economics, particularly in the sub-discipline of econometrics, which aims to identify causal relations from non-experimental economic data. In this part of the course, the main approaches to causality in econometrics will be presented, critically compared and contrasted to each other. In particular, Granger causality (where causal order is based on time order) and structural approach (where causal order is based on invariance-to-interventions) will be presented. These two key approaches represent the two dominant strands of causality in econometrics, and both have affinities with philosophical treatments of probabilistic causality. Granger causality is close to Suppes' probabilistic causality, while the structural approach, relates closely to work on causality by Cartwright, Woodward and others. This course will be concluded by analysis of how methods of causal inference work in econometrics for both kinds of causality presented above.

  5. Presentation by participants
    In this section, planned to take place at the end of the course, participants will give short (10 min.) presentations of their research topic, which will be commented on by faculty and participants of the summer school.

Download the Reading List here.