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2021

Tasha Fairfield presents "Recasting the Debate on COVID-19 Origins in Bayesian Terms", at https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09, Wednesday, April 27, 2022

The debate on covid-19 origins has been politically fraught. Yet setting aside conspiracy theories and the most implausible of the lab-leak hypotheses, there is significant disagreement among qualified experts.  Some are adamant that the case should be considered closed in favor of zoonosis, while others view the evidence as weak, even if they concede that prior knowledge about previous epidemics favors zoonosis, and a few maintain that some sort of laboratory leak is a firm possibility.   

This project applies the methodology developed in Social Inquiry and...

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Katharina Fellnhofer presents "A framework for measuring intuitive decision making in real-world contexts", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, April 20, 2022
Intuition refers to the ability to use nonconscious information for conscious decision making. The nonconscious element has predominantly been measured by its speed of operation and ease of application. Only a few scholarly attempts at behavioral measuring take nonconsciousness into account, and they use situations that do not represent the real world, which limits generalization. In my talk, I will present the results of my intuition measurement using a within-subject design with real investment opportunities that employ hidden images as nonconscious... Read more about Katharina Fellnhofer presents "A framework for measuring intuitive decision making in real-world contexts"
Deirdre Bloome presents "Rising Class Crystallization? Trends in Multidimensional Class Inequality across Racialized/Ethnic Groups", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, April 13, 2022
In recent decades, U.S. income and wealth inequality grew, educational attainment rose, and occupational structures shifted. Because these dimensions of social class are intertwined---with higher education often generating higher income, wealth, and occupational prestige---rising inequality in one may have pushed some people toward the tops of multiple hierarchies, and others toward the bottoms of multiple hierarchies (polarizing people in the multidimensional space of class inequality). Are people occupying increasingly consistent positions across multiple class hierarchies? And... Read more about Deirdre Bloome presents "Rising Class Crystallization? Trends in Multidimensional Class Inequality across Racialized/Ethnic Groups"
Adeline Lo presents "Refugees in Modern Media", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, April 6, 2022
The effects of refugee migration permeates most aspects of a recipient society, not least native inclusionary attitudes and behaviors towards refugees. While recent research has emphasized measuring the extent to which direct exposure to refugees affects inclusion, much less is known about the more frequent type of refugee exposure natives experience: exposure to refugees through media representation. This project establishes key patterns to how much and in what ways modern media represents refugee stories, how this has changed over time, and explores how major shifts in the ways refugee... Read more about Adeline Lo presents "Refugees in Modern Media"
Hannah Druckenmiller presents "Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences", at https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09, Wednesday, March 30, 2022
We develop a simple cross-sectional research design to identify causal effects that is robust to unobservable heterogeneity. When many observational units are dense in physical space, it may be sufficient to regress the “spatial first differences” (SFD) of the outcome on the treatment and omit all covariates. This approach is conceptually similar to first differencing approaches in time-series or panel models, except the index for time is replaced with an index for locations in space. The SFD design identifies plausibly causal effects, so long as local changes in the treatment and... Read more about Hannah Druckenmiller presents "Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences"
José R. Zubizarreta presents "Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, March 23, 2022
A fundamental principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Across the health and social sciences, statistical methods for covariate adjustment are used in pursuit of this principle. Typical methods are matching, regression, and weighting. In this talk, we will examine the connections between these methods through their underlying mathematical programs. We will study their strengths and weaknesses in terms of study design, computational tractability, and statistical efficiency.... Read more about José R. Zubizarreta presents "Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference"
Iavor Bojinov presents "Design and Analysis of Switchback Experiments", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, March 9, 2022

Switchback experiments, where a firm sequentially exposes an experimental unit to random treatments, are among the most prevalent designs used in the technology sector, with applications ranging from ride-hailing platforms to online marketplaces. Although practitioners have widely adopted this technique, the derivation of the optimal design has been elusive, hindering practitioners from drawing valid causal conclusions with enough statistical power. We address this limitation by deriving the optimal design of switchback experiments under a range of different assumptions on the order of...

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Sharad Goel presents "Designing Equitable Algorithms for Criminal Justice and Beyond", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, March 2, 2022
Machine learning algorithms are now used to automate routine tasks and to guide high-stakes decisions, but, if not carefully designed, they can exacerbate inequities. I’ll start by describing an evaluation of automated speech recognition (ASR) tools, which power popular virtual assistants, facilitate automated closed captioning, and enable digital dictation platforms for health care. We find that five state-of-the-art ASR systems -- developed by Amazon, Apple, Google, IBM, and Microsoft -- exhibited substantial racial disparities, making twice as many errors for Black speakers compared to... Read more about Sharad Goel presents "Designing Equitable Algorithms for Criminal Justice and Beyond"
Soroush Saghafian presents "Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, February 23, 2022

A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements. Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process and enable researchers to find guidelines that are both personalized and dynamic. However, available methods in finding optimal DTRs often rely on assumptions that...

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Edward McFowland III presents "Anomalous Pattern Detection: A Novel Lens for Scientific Inquiry", at CGIS Knafel Building (K354) - 12:10-1:30 pm, Wednesday, February 16, 2022
There has been a growing interest in the use of machine learning methods for causal inference, which often involves adjusting or reappropriating predictive models, with causality in mind. As an alternative, anomaly detection methods offer a unique lens through which to conduct causal inference, as the presence of a causal effect results in treatment group units that appear anomalous in comparison to the control group. Moreover, anomalous pattern detection intentionally localizes the presence of treatment effects, which has tremendous value when the ultimate goal involves hypothesis generation... Read more about Edward McFowland III presents "Anomalous Pattern Detection: A Novel Lens for Scientific Inquiry"

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