eventSeminar
Tim de Silva | AI Financial Advice: Supply, Demand, and Life Cycle Implications
SeminarOct 07, 202612:00 PM - 1:15 PM
October
07
2026
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This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Free and open to the public
Tuesday, November 8, 2022 [Link to join]
(ID: 996 2837 2037, Password: 386638)
Speaker: Luke Miratrix (Harvard University)
Title: A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step
Discussant: Laura Hatfield (Harvard University)
Abstract: The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption that the treatment group, absent treatment, would change "similarly" to the control group over time. To increase the plausibility of this assumption, a natural idea is to match treated and control units prior to a DiD analysis. In this paper, we characterize the bias of matching under a class of linear structural models with both observed and unobserved confounders that have time varying effects. Given this framework, we find that matching on baseline covariates generally reduces the bias associated with these covariates, when compared to the original DiD estimator. We further find that additionally matching on pre-treatment outcomes has both cost and benefit. First, matching on pre-treatment outcomes will partially balance unobserved confounders, which mitigates some bias. This reduction is proportional to the outcome's reliability, a measure of how coupled the outcomes are with the latent covariates. On the other hand, we find that matching on pre-treatment outcomes also undermines the second "difference" in a DiD estimate by forcing the treated and control group's pre-treatment outcomes to be equal. This injects bias into the final estimate, creating a bias-bias tradeoff. We extend our bias results to multivariate confounders with multiple pre-treatment periods and find similar results. We summarize our findings with heuristic guidelines on whether to match prior to a DiD analysis, along with a method for roughly estimating the reduction in bias. We illustrate our guidelines by reanalyzing a recent empirical study that used matching prior to a DiD analysis to explore the impact of principal turnover on student achievement.
