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Miech on 'generational forgetting' about drug-use dangers

Impacts of H-1B visas: Lower prices and higher production - or lower wages and higher profits?

MTF data show 10% of 19-20 year-olds report bouts of drinking 10-plus alcoholic beverages

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Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

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Next Brown Bag

Mon, Feb 13, 2017, noon:
Daniel Almirall, "Getting SMART about adaptive interventions"

Model-Free Monte Carlo-like Policy Evaluation

Publication Abstract

Fonteneau, R., Susan A. Murphy, L. Wehenkel, and D. Ernst. 2010. "Model-Free Monte Carlo-like Policy Evaluation." In Volume 9: AISTATS 2010 Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. San Francisco: Morgan Kaufmann Publishers.

We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of "broken trajectories" made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions.

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