Presentations

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...

Read more about Alex Tarr presents "Estimating Average Treatment Effects with Support Vector Machines"
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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Cory McCartan presents "Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans", at https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09, Wednesday, November 11, 2020

Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to large maps with many districts, incorporate realistic legal constraints, and accurately sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these three areas. We present a new Sequential Monte Carlo (SMC) algorithm that...

Read more about Cory McCartan presents "Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans"
Yiling Chen presents "Unexpected Consequences of Algorithm-in-the-Loop Decision Making", at https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09, Wednesday, November 4, 2020

The rise of machine learning has fundamentally altered decision making: rather than being made solely by people, many important decisions are now made through an “algorithm-in-the-loop” process where machine learning models inform people. Yet insufficient research has considered how the interactions between people and models actually influence human decision making. In this talk, I’ll discuss results from a set of controlled experiments on algorithm-in-the-loop human decision making in two contexts (pretrial release and financial lending). For example, when presented with algorithmic...

Read more about Yiling Chen presents "Unexpected Consequences of Algorithm-in-the-Loop Decision Making"

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