Presentations

Isabel Fulcher presents "Using routinely collected data to quantify the burden of COVID-19: proceed, but with caution", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, April 28, 2021

Valid estimates for the number of SARS-CoV-2 infections is imperative for assessing the impact of the COVID-19 pandemic within specific populations. Here, we discuss ongoing efforts aimed at understanding the state of the pandemic in two different contexts. First, we focus on seven low- and middle-income countries where COVID-19 testing has been limited. We propose using aggregated health systems data to perform syndromic surveillance and detect potential outbreaks. Second, we focus locally on the City of Holyoke, Massachusetts where testing is readily available, but racial and ethnic...

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Kristen Hunter presents "Conceptualizing experimental controls using the potential outcomes framework", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, April 21, 2021

The goal of a well-controlled study is to remove unwanted variation when estimating the causal effect of the intervention of interest. Experiments conducted in the basic sciences frequently achieve this goal using experimental controls, such as "negative'' and "positive'' controls, which are measurements designed to detect systematic sources of unwanted variation. Here, we introduce clear, mathematically precise definitions of experimental controls using potential outcomes. Our definitions provide a unifying statistical framework for fundamental concepts of experimental design from the... Read more about Kristen Hunter presents "Conceptualizing experimental controls using the potential outcomes framework"
Laura Forastiere presents "Heterogeneous Treatment and Spillover Effects under Clustered Network Interference", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, April 14, 2021
The bulk of causal inference studies rules out the presence of interference between units. However, in many real-world scenarios units are interconnected by social, physical or virtual ties and the effect of a treatment can spill from one unit to other connected individuals in the network. In these settings, interference should be taken into account to avoid biased estimates of the treatment effect, but it can also be leveraged to save resources and provide the intervention to a lower percentage of the population where the treatment is more effective and where the effect can spill over to... Read more about Laura Forastiere presents "Heterogeneous Treatment and Spillover Effects under Clustered Network Interference"
David Ham presents "Using Machine Learning to Test Hypothesis in Conjoint Analysis", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, April 7, 2021

Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other relevant factors, impacts decision-making. Currently, there exist two methodological approaches to analyzing data from a conjoint experiment. The first focuses on estimating marginal effects of each factor while averaging over the other factors.  Although this allows for straightforward nonparametric estimation using a design-based approach, the results critically depend on the distribution of other factors...

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Avi Feller presents "Varying impacts of letters of recommendation on college admissions: Approximate balancing weights for subgroup effects in observational studies", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, March 24, 2021
In a pilot program during the 2016-17 admissions cycle, the University of California, Berkeley invited many applicants for freshman admission to submit letters of recommendation. We use this pilot as the basis for an observational study of the impact of submitting letters of recommendation on subsequent admission, with the goal of estimating how impacts vary across pre-defined subgroups. Understanding this variation is challenging in observational studies, however, because estimated impacts reflect both actual treatment effect variation and differences in covariate balance across groups. To... Read more about Avi Feller presents "Varying impacts of letters of recommendation on college admissions: Approximate balancing weights for subgroup effects in observational studies"
Suzanna Linn presents "Causal Inference in Dynamic Systems", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, March 17, 2021

A causal inference revolution has been under way in political methodology for the better part of the last decade. Time series analysts have not been major contributors to this revolution because the tools that have been developed thus far do not fit our data. Existing methods either require analyst to pool observations so that analysts can differentiate treatment and control units, require analysts to identify or develop suitable control series, or require the analyst to exercise control over treatment in a time series experiment. Our goal is to identify the assumptions and conditions...

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Adeline Lo presents "Mixed-Membership Stochastic Blockmodels for Bipartite Networks: Application to Cosponsorship in the US Senate", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, March 10, 2021

Many networks in political and social research are naturally bipartite — with two distinct types of actors (nodes), and edges connecting exclusively across the actor types. An example of such networks is the one that results from cosponsorship decisions, in which legislators are connected to the bills they support. Typically, researchers who wish to study these networks are forced to "project" or marginalize over one node type, in an effort to form the kinds of unipartite networks that standard models can handle. This can result in aggregation bias and loss of relevant information...

Read more about Adeline Lo presents "Mixed-Membership Stochastic Blockmodels for Bipartite Networks: Application to Cosponsorship in the US Senate"
Arthur Yu presents "Beyond LATE: Identification of ATEs of Always-Takers and Never-Takers", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, March 3, 2021
In the presence of heterogeneous treatment effects, instrumental variable (IV) estimation point identifies the local average treatment effect, an average treatment effect (ATE) for compliers. This paper provides a set of identification results that extrapolate the LATE to the ATEs of always-takers and never-takers. We first show that the ATEs of always-takers and never-takers can be written as the weighted average of marginal treatment effect (MTE) functions. We then demonstrate that, under additional parametric assumptions on these MTE functions, we can point identify the ATEs of always-... Read more about Arthur Yu presents "Beyond LATE: Identification of ATEs of Always-Takers and Never-Takers"
James Robins presents "Estimation of Optimal Testing and Treatment Regimes under No Direct Effect (NDE) of Testing", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, February 24, 2021

In this talk I describe new, highly efficient estimators of optimal joint testing and treatment regimes under the no direct effect assumption that a given laboratory, diagnostic, or screening test has no effect on a patient's clinical outcomes, except through the effect of the test results on the choice of treatment. The proposed estimators attain high efficiency because they leverage this "no direct effect of testing" (abbreviated as NDE) assumption.

What is surprising and, indeed, unprecedented in my experience, is that, in a substantive study of HIV infected subjects, our...

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Soichiro Yamauchi presents "Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, February 17, 2021
Marginal structural models are a popular tool for investigating the effects of time-varying treatments, but they require the assumption that there are no unobserved confounders between the treatment and outcome. With observational data, this assumption may be difficult to maintain, and in studies with panel data, many researchers use fixed effects models to purge the data of time-constant unmeasured confounding. Unfortunately, traditional linear fixed effects models are not suitable for marginal structural models, since they can only estimate lagged effects under implausible assumptions. To... Read more about Soichiro Yamauchi presents "Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects"

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