CORY McCARTAN

I'm a Ph.D. candidate in statistics at Harvard University, working with Prof. Kosuke Imai. My methodological research focuses on Bayesian modeling, causal inference, and sampling, and my applied projects are centered around redistricting, political geography, and fairness. As part of the Algorithm-Assisted Redistricting Methodology (ALARM) Project, I work on open-source R software for redistricting and political analysis.

Papers

Individual and Differential Harm in Redistricting. With Christopher T. Kenny. Replication code

Abstract

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 plan. Harm improves on existing measures by both focusing on the choices of individual voters and directly incorporating counterfactual plans. We discuss strategies for estimating harm, and demonstrate the utility of our framework through analyses of partisan gerrymandering in New Jersey, voting rights litigation in Alabama, and racial dynamics of Boston City Council elections.

Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans. Under Review. With Kosuke Imai. Software implementation.
Covered by The Washington Post.

Abstract

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 and efficiently sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these areas. We present a new Sequential Monte Carlo (SMC) algorithm that generates a sample of redistricting plans converging to a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevant space of redistricting plans better than the existing Markov chain Monte Carlo (MCMC) algorithms that generate plans sequentially. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania. We find that the proposed algorithm converges to the target distribution faster and with fewer samples than a state-of-the-art MCMC algorithm. Open-source software is available for implementing the proposed methodology.

The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census. 2021. Science Advances 7(41), eabk3283. With Christopher T. Kenny, Shiro Kuriwaki, Evan T. R. Rosenman, Tyler Simko, and Kosuke Imai. Originally a Public Comment to the Census Bureau (May 28, 2021). FAQ; Reaction to the Bureau’s Response; Supplementary information; Replication materials.
Covered by The Washington Post, the Associated Press, the San Francisco Chronicle, NC Policy Watch, and others.

Abstract

Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.

Measuring and Modeling Neighborhoods. Under Review. With Jacob R. Brown and Kosuke Imai. Survey tool; Poster

Abstract

With the availability of granular geographical data, social scientists are increasingly interested in examining how residential neighborhoods are formed and how they influence attitudes and behavior. To facilitate such studies, we develop an easy-to-use online survey instrument that allows respondents to draw their neighborhoods on a map. We then propose a statistical model to analyze how the characteristics of respondents, relevant local areas, and their interactions shape subjective neighborhoods. The model also generates out-of-sample predictions of one's neighborhood given these observed characteristics. We illustrate the proposed methodology by conducting a survey among registered voters in Miami, New York City, and Phoenix. We find that across these cities voters are more likely to include same-race and co-partisan census blocks in their neighborhoods. Net of other factors, White respondents are 6.1 to 16.9 percentage points more likely to include in their neighborhoods a census block composed entirely of White residents compared to one with no White residents. Similarly, Democratic and Republican respondents are 8.6 to 19.2 percentage points more likely to include an entirely co-partisan census block compared to one consisting entirely of out-partisans. Co-partisanship exhibits a similar, independent, influence. We also show that our model provides more accurate out-of-sample predictions than the standard distance-based measures of neighborhoods. Open-source software is available for implementing the proposed methodology.

Widespread Partisan Gerrymandering Mostly Cancels in Aggregate, but Reduces Competition and Responsiveness. Under Review. With Christopher T. Kenny, Tyler Simko, Shiro Kuriwaki, and Kosuke Imai.

Abstract

Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To isolate the electoral impact of gerrymandering from the effects of other factors including geography and redistricting rules, we compare predicted election outcomes under the enacted plan with those under a large sample of non-partisan, simulated alternative plans for all states. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the bias it creates cancels at the national level, giving Republicans two additional seats, on average. In contrast, moderate pro-Republican bias due to geography and redistricting rules remains. Finally, we find that partisan gerrymandering reduces electoral competition and makes the House's partisan composition less responsive to shifts in the national vote.

Simulated redistricting plans for the analysis and evaluation of redistricting plans in the United States. Under Review. With Christopher Kenny, Tyler Simko, Shiro Kuriwaki, George Garcia III, Kevin Wang, Melissa Wu, and Kosuke Imai. Project website; Replication code; Data

Abstract

A collection of simulated congressional districting plans and underlying code developed by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. The data allow for the evaluation of enacted and other congressional redistricting plans in the United States. While the use of redistricting simulation algorithms has become standard in academic research and court cases, any simulation analysis requires non-trivial efforts to combine multiple data sets, identify state-specific redistricting criteria, implement complex simulation algorithms, and summarize and visualize simulation outputs. We have developed a complete workflow that facilitates this entire process of simulation-based redistricting analysis for the congressional districts of all 50 states. The resulting data include ensembles of simulated 2020 congressional redistricting plans and necessary replication data. We provide the underlying code, which serves as a template for customized analyses. All data and code are free and publicly available.

Recalibration Of Predicted Probabilities Using the “Logit Shift”: Why does it work, and when can it be expected to work well? Forthcoming in Political Analysis. With Evan T. R. Rosenman and Santiago Olivella.

Abstract

The output of predictive models is routinely recalibrated by reconciling low-level predictions with known quantities defined at higher levels of aggregation. For example, models predicting vote probabilities at the individual level in U.S. elections can be adjusted so that their aggregation matches the observed vote totals in each county, thus producing better calibrated predictions. In this research note, we provide theoretical grounding for one of the most commonly used recalibration strategies, known colloquially as the “logit shift.” Typically cast as a heuristic adjustment strategy (whereby a constant correction on the logit scale is found, such that aggregated predictions match target totals), we show that the logit shift offers a fast and accurate approximation to a principled, but computationally impractical adjustment strategy: computing the posterior prediction probabilities, conditional on the observed totals. After deriving analytical bounds on the quality of the approximation, we illustrate its accuracy using Monte Carlo simulations. We also discuss scenarios in which the logit shift is less effective at recalibrating predictions: when the target totals are defined only for highly heterogeneous populations, and when the original predictions correctly capture the mean of true individual probabilities, but fail to capture the shape of their distribution.

Geodesic Interpolation on Sierpinski Gaskets. 2021. Journal of Fractal Geometry 8(2), 117-152. With Caitlin M. Davis, Laura A. LeGare, and Luke G. Rogers.

Abstract

We study the analogue of a convex interpolant of two sets on Sierpiński gaskets and an associated notion of measure transport. The structure of a natural family of interpolating measures is described and an interpolation inequality is established. A key tool is a good description of geodesics on these gaskets, some results on which have previously appeared in the literature.

Works in Progress

Estimation of Racial Disparities When Race is Not Observed. With Robin Fisher, Daniel E. Ho, Jacob Goldin, and Kosuke Imai. Poster

Abstract

Bayesian Improved Surname Geocoding (BISG) is widely used to provide estimates of racial disparities from microdata that do not contain individual race information. However, in most applications, the existing approach to estimating these disparities relies on an implausible independence assumption. We provide an alternative identifying assumption and develop a Bayesian model that can provide accurate estimates of racial disparities. We apply the proposed method to the problem of estimating party identification by race in North Carolina and find that it improves significantly on the existing approach.

Two-stage Experiments and Stochastic Intervention. With Shusei Eshima and Kosuke Imai.

Regression of the Conditional Median. With Xiao-Li Meng.

Algorithm-Assisted Redistricting Methodology (book). With Kosuke Imai, Christopher Kenny, and Tyler Simko.

Software

redist: Simulation Methods for Legislative Redistricting
This R package enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package supports various constraints in the redistricting process, such as geographic compactness and population parity requirements. Tools for analysis, including computation of various summary statistics and plotting functionality, are also included.

redistmetrics: Redistricting Metrics
Reliable and flexible tools for scoring redistricting plans using common measures and metrics. These functions provide key direct access to tools useful for non-simulation analyses of redistricting plans, such as for measuring compactness or partisan fairness. Tools are designed to work with the redist package seamlessly.

easycensus: Quickly Find, Extract, and Marginalize U.S. Census Tables
Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process by providing functions to quickly locate variables and download labeled tables from the Census APIs.

PL94171: Tabulate P.L. 94-171 Redistricting Data Summary Files
Tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is.


adjustr: Stan Model Adjustments and Sensitivity Analyses using Importance Sampling
Functions to help assess the sensitivity of a Bayesian model (fitted using the rstan package) to the specification of its likelihood and priors. Users provide a series of alternate sampling specifications, and the package uses Pareto-smoothed importance sampling to estimate posterior quantities of interest under each specification.

conformalbayes: Jackknife(+) Predictive Intervals for Bayesian Models
Provides functions to construct finite-sample calibrated predictive intervals for Bayesian models, following the approach in Barber et al. (2021). These intervals are calculated efficiently using importance sampling for the leave-one-out residuals. By default, the intervals will also reflect the relative uncertainty in the Bayesian model, using the locally-weighted conformal methods of Lei et al. (2018).

alarmdata: Download, Merge, and Process Redistricting Data
Utility functions to download and process data produced by the ALARM Project, including 2020 redistricting files and 50-State Redistricting Simulations.


blockpop: Estimate Census Block Populations for 2020
2020 Census data is delayed and affected by differential privacy. This package uses FCC block-level population estimates from 2010–2019, which are based on new roads and map data, along with decennial Census and ACS data, to estimate 2020 block populations, both overall and by major race/ethnicity categories (using iterative proportional fitting).

ggredist: Scales, Palettes, and Extensions of ggplot2 for Redistricting
Provides ggplot2 extensions for political mapmaking, including new geometries, easy label generation and placement, automatic map coloring, and map scales, palettes, and themes.


tinytiger: Lightweight Interface to TIGER/Line Shapefiles
Download geographic shapes from the United States Census Bureau TIGER/Line Shapefiles. Functions support downloading and reading in geographic boundary data. All downloads can be set up with a cache to avoid multiple downloads. Data is available back to 2000 for most geographies.

wacolors: Colorblind-friendly Palettes from Washington State

Other Writing

Candy cane shortages and the importance of variation. December 21, 2021. International Statistical Institute: Statisticians React to the News.

Where will the rocket land? May 12, 2021. International Statistical Institute: Statisticians React to the News.

Who’s the most electable Democrat? It might be Warren or Buttigieg, not Biden. October 23, 2019. The Washington Post.

I-405 Express Toll Lanes: Usage, benefits, and equity. 2019. Technical report for the Washington State Department of Transportation. With Shirley Leung, C.J. Robinson, Kiana Roshan Zamir, Vaughn Iverson, and Mark Hallenbeck. Project website

Project summary

Congestion is increasing in cities around the country, and particularly in the Seattle region. Local governments are increasingly experimenting with congestion pricing schemes to manage congestion. The Washington State Department of Transportation (WSDOT) opened a congestion pricing facility in 2015 on I-405, which runs through the eastern suburbs of Seattle. The facility operates by selling extra space in the high-occupancy vehicle (HOV) lanes to single-occupancy vehicles (SOVs), and dynamically changing the price of entry to manage demand and keep the lanes operating. These combined HOV and tolled SOV lanes are called High Occupancy Tolling (HOT) lanes.

While the HOT lanes have been operative for over three years, there has been little research into the equity impacts of the lanes. Using data on each trip made on the I-405 HOT lanes in 2018, demographic data on census block groups, and lane speed, volume, and travel time data, we tried to answer this question. We studied how the express toll lanes are used, the benefits they provide to the region, and how these benefits are distributed among different groups of users.

Contact

  cmccartan@fas.harvard.edu

  @CoryMcCartan

  Science Center, Ste. 400
      1 Oxford St.
      Cambridge MA 02138