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DTSTART;TZID=America/New_York:20261117T140000
DTEND;TZID=America/New_York:20261117T160000
DTSTAMP:20260829T181930Z
CREATED:20260819T195033Z
LAST-MODIFIED:20260829T181930Z
UID:10004039-1794924000-1794931200@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Mathematical Economics Seminar: Experimental Design in Networks
DESCRIPTION:Speaker: Christopher Harshaw (c.harshaw@columbia.edu)\, Columbia University \nTitle: Design-Based Minimax Theory for Network Experiments \nAbstract: Network experiments are used throughout the social and medical sciences to investigate causal effects under the presence of interference. While a large body of work has developed improved statistical procedures\, the fundamental limits of statistical estimation in these settings is less well understood. In this paper\, we develop and investigate a design-based theory of minimax risk for network experiments under an arbitrary neighborhood interference model. Our notion of minimax risk describes the optimal precision among all statistical procedures for investigating a particular causal effect on the observed interference network. We show that the minimax risk is a function of the corresponding conflict graph\, which captures inherent unobservability of estimand-relevant potential outcomes given the observed interference network. Our main contribution is a series of upper and lower bounds on the minimax rate in terms of local and global connectivity properties of the conflict graph. To illustrate their utility\, we apply these general results to obtain minimax analyses for two commonly studied effects: the direct treatment effect and global average treatment effect. \n  \nSpeaker: Evan Munro (evan.munro@chicagobooth.edu)\, University of Chicago\, Booth School of Business \nTitle: Robust Signal Maximization in Spillover Experiments \nAbstract: We study the optimal design and analysis of experiments for estimating spillover effects. Assuming a known (e.g.\, linear) exposure mapping\, we characterize the treatment-assignment distribution and regression-based estimator that minimize worst-case asymptotic variance against a broad class of distributions of unobservables. The design problem yields an intuitive solution in which the planner trades off spillover signal strength against diffusion of spillover variation. The analysis problem yields a simple recentered instrumental variable estimator to best leverage this variation. This framework produces natural solutions in several benchmark cases – such as clustered exposure – and suggests computationally tractable approximations for general networks\, including bipartite settings. We illustrate these new tools in semi-synthetic experiments based on two applications from development economics. Our approach yields large standard error reductions in both experiments\, increasing effective sample sizes by 50-100% or more.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/mathecon_111726/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Economics Seminar
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DTSTART;TZID=America/New_York:20261208T140000
DTEND;TZID=America/New_York:20261208T160000
DTSTAMP:20260902T182726Z
CREATED:20260829T181947Z
LAST-MODIFIED:20260902T182726Z
UID:10004040-1796738400-1796745600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:Mathematical Economics Seminar
DESCRIPTION:Speaker: Karun Adusumilli\, University of Pennsylvania\,\nTitle: Continuous Time Asymptotic Representations for Adaptive Experiments\nAbstract: This article develops a continuous-time asymptotic framework for analyzing adaptive experiments—settings in which data collection and treatment assignment evolve dynamically in response to incoming information. Akey challenge in analyzing fully adaptive experiments\, where the assignment policy is updated after each observation\, is that the sequence of policy rules often lack a well-defined asymptotic limit. To address this\, we focus instead on the empirical allocation process\, which captures the (normalized) number of observations assigned to each treatment over time. We show that\, under general conditions\, any adaptive experiment and its associated empirical allocation process can be approximated by a limit experiment defined by Gaussian diffusions with unknown drifts and a corresponding continuous-time allocation process. This limit representation facilitates the analysis of optimal decision rules by reducing the dimensionality of the state-space and exploiting the tractability of Gaussian diffusions. We apply the framework to derive optimal estimators\, analyze in-sample regret for adaptive experiments\, and construct e-processes for anytime-valid inference. Notably\, we introduce the first definition of any-time and any-experiment valid inference for multi-treatment settings. \nSpeaker: Toru Kitagawa\, Brown University\,\nTitle: TBA \nSpeaker: Chen Qiu\, Cornell University\,\nTitle: Local Asymptotics for Treatment Choice with Partial Identification\nAbstract: We provide a new asymptotic framework to derive approximately optimal treatment assignments when sampling noise from data is compounded by fundamental uncertainty due to partial identification. We recenter the reduced-form parameter around its least-favorable configuration and consider drifting parameter sequences that yield both diminishing levels of sampling uncertainty and of partial identification. We characterize the limiting decision problem as a normal location shift model with a suitable limiting identified set. We apply our results to treatment choice problems with contaminated outcomes\, to robust welfare analyses with partially identified consumer surplus\, and to the problem of aggregating experimental estimates for policy adoption.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/mathecon_12826/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Mathematical Economics Seminar
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