Presentations

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...

Read more about James Robins presents "Estimation of Optimal Testing and Treatment Regimes under No Direct Effect (NDE) of Testing"
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"
Dean Knox presents "ε-sharp Bounds for Partially Observed Causal Processes: Testing for Racial Bias in Policing by Fusing Incomplete Records", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, February 10, 2021
Social scientists often possess fragmented information about subsets and aspects of the complex causal processes they study. Research on police-civilian interactions, for example, is complicated not only by undocumented interactions, but inconsistent recording of events within documented interactions. These data constraints can lead to a proliferation of incompatible analytic approaches relying on contradictory unstated assumptions, impeding scientific progress on important questions like the severity of racial bias in policing. Nonparametric sharp bounds, or the tightest possible range of... Read more about Dean Knox presents "ε-sharp Bounds for Partially Observed Causal Processes: Testing for Racial Bias in Policing by Fusing Incomplete Records"
Alex Tarr presents "Estimating Average Treatment Effects with Support Vector Machines", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, February 3, 2021

Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption. We show that the SVM cost parameter controls the trade-off between covariate balance and subset size, and as a result, existing SVM regularization path algorithms can be used to compute the balance-sample size frontier. We then characterize the bias of causal effect estimation arising from this tradeoff, connecting the proposed SVM procedure...

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Guillaume Basse presents "Displacement Effects in a Hot Spot Policing Intervention in Medellin: Inference and Pitfalls", at https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09, Wednesday, January 27, 2021
In hot policing, resources are targeted at specific locations predicted to be at high risk of crime; so-called "hot spots." Rather than reduce overall crime, however, there is a concern that these interventions simply displace crime from the targeted locations to nearby non-hot spots. We address this question in the context of a large-scale randomized experiment in Medellin, Colombia, in which police were randomly assigned to increase patrols at a subset of possible hotspots. Estimating the displacement effects on control locations is difficult because the probability that a nearby hotspot is... Read more about Guillaume Basse presents "Displacement Effects in a Hot Spot Policing Intervention in Medellin: Inference and Pitfalls"
Kosuke Imai presents "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment", at https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09, Wednesday, December 2, 2020
Despite an increasing reliance on fully-automated algorithmic decision making in our day-to-day lives, human beings still make highly consequential decisions.  As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers in order to guide their decisions.  While there exists a fast growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions.  We develop a statistical methodology for experimentally... Read more about Kosuke Imai presents "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment"
Tyler VanderWeele presents "Revisiting Psychometric Theory and Factor Analysis", at https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09, Wednesday, November 18, 2020

The presentation will revisit some of the conceptual and statistical foundations of psychometric measurement theory and factor analysis, specifically addressing the questions: (i) What happens to “factors” when they causally affect one another?, (ii) Is an underlying univariate latent variable a reasonable model for psycho-social constructs?, (iii) What are the testable empirical implications of such a model? and (iv) What alternative interpretations of analyses with constructed measures might be possible?

The presentation will be based upon the following three preprints:...

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