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Economics Job Talk

Tuesday, January 10, 2023
4:00pm to 5:00pm
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Baxter Lecture Hall
Design-based Estimation Theory for Complex Experiments
Haoge Chang, PhD Candidate in Economics, Yale University,

Abstract: This paper considers the estimation of treatment effects in randomized experiments with complex experimental designs, including cases with interference between units. We develop a design-based estimation theory for arbitrary experimental designs. Our theory facilitates the analysis of many design-estimator pairs that researchers commonly employ in practice and provide procedures to consistently estimate asymptotic variance bounds. We propose new classes of estimators with favorable asymptotic properties from a design-based point of view. In addition, we propose a scalar measure of experimental complexity which can be linked to the design-based variance of the estimators. We demonstrate the performance of our estimators using simulated datasets based on an actual network experiment studying the effect of social networks on insurance adoptions.

For more information, please contact Barbara Estrada by phone at 626-395-4083 or by email at [email protected].