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Sufficient covariates and linear propensity analysis

This video was recorded at 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia 2010. Working within the decision-theoretic framework for causal inference, we study the properties of "sufficient covariates", which support causal inference from observational data, and possibilities for their reduction. In particular we illustrate the role of a propensity variable by means of a simple model, and explain why such a reduction typically does not increase (and may reduce) estimation efficiency.

Keywords:: videolectures, ocwc, oec

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Science and Technology / Computer Science / Programming & Programming Languages

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Author:: Hui Guo, Faculty of Mathematics, University of Cambridge
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States