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Dae Woong Ham and Luke Miratrix present "A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step." Wednesday, September 28, 2022
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... Read more about Dae Woong Ham and Luke Miratrix present "A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step."
Matthew Blackwell presents "Difference-in-differences Designs for Controlled Direct Effects" Wednesday, September 21, 2022
Political scientists are increasingly interested in controlled direct effects, which are important quantities of interest for understanding why, how, and when causal effects will occur. Unfortunately, their identification has usually required strong and often unreasonable selection-on-observeables assumptions for the mediator. In this paper, we show how to identify and estimate controlled direct effects under a difference-in-differences design where we have measurements of the outcome and mediator before and after treatment assignment. This design allows us to weaken the identification... Read more about Matthew Blackwell presents "Difference-in-differences Designs for Controlled Direct Effects"
Cory McCartan presents "Individual and Differential Harm in Redistricting" Wednesday, September 14, 2022
Social scientists have developed dozens of measures for assessing partisan bias in redistricting. But these measures cannot be easily adapted to other groups, including those defined by race, class, or geography. Nor are they applicable to single- or no-party contexts such as local redistricting. To overcome these limitations, we propose a unified framework of harm for evaluating the impacts of a districting plan on individual voters and the groups to which they belong. We consider a voter harmed if their chosen candidate is not elected under the current plan, but would be under a different... Read more about Cory McCartan presents "Individual and Differential Harm in Redistricting"
Xiang Zhou presents "Marginal Interventional Effects" Wednesday, September 7, 2022

 Conventional causal estimands, such as the average treatment effect (ATE), reflect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often incremental, changing the treatment status for only a small segment of the population who are at or near “the margin of participation.” To capture this notion, two parallel lines of inquiry have developed in economics and in statistics and epidemiology that define, identify, and estimate what we call interventional effects. In this article,...

Read more about Xiang Zhou presents "Marginal Interventional Effects"
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...

Read more about Tasha Fairfield presents "Recasting the Debate on COVID-19 Origins in Bayesian Terms"
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"

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