Information Recovery and Causality: A Tribute to George Judge

Posted: 11 Oct 2016

See all articles by Gordon C. Rausser

Gordon C. Rausser

University of California, Berkeley - Department of Agricultural and Resource Economics

David Bessler

Texas A&M University, College Station - Department of Agricultural Economics

Date Written: October 2016

Abstract

In Professor George Judge's pursuit of information recovery and isolating causality in noisy effects observational data, there is a critical distinction between deductive and inductive empirical analysis. For the former, we bring together a synthesis of the literature that has emerged since Koopmans' measurement with theory philosophy. For the latter, we present a host of methodologies that attempt to isolate the causal mechanisms existing in patterns revealed in noisy measurement data. The deductive focus is limited by available theoretical constructs, whereas the inductive focus is fraught with data mining complications, ultimately finding its potential validation in forecasting.

Suggested Citation

Rausser, Gordon C. and Bessler, David, Information Recovery and Causality: A Tribute to George Judge (October 2016). Annual Review of Resource Economics, Vol. 8, Issue 1, pp. 7-23, 2016, Available at SSRN: https://ssrn.com/abstract=2850947 or http://dx.doi.org/10.1146/annurev-resource-121615-032137

Gordon C. Rausser (Contact Author)

University of California, Berkeley - Department of Agricultural and Resource Economics ( email )

207 Giannini Hall no. 3310
Berkeley, CA 94720
United States
510-642-6591 (Phone)
510-643-0287 (Fax)

HOME PAGE: http://are.berkeley.edu/~rausser/

David Bessler

Texas A&M University, College Station - Department of Agricultural Economics ( email )

College Station, TX 77840
United States
979-845-3096 (Phone)

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