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Case investigation and contact tracing (CICT) is an important tool for communicable disease control, both to proactively interrupt chains of transmission and to collect information for situational awareness. We run the first randomized trial of COVID-19 CICT to investigate the utility of manual (Between December 15, 2021 and February 5, 2022, a stepped wedge cluster randomized trial was run in which Santa Clara County ZIP Codes progressively transitioned from manual to automated CICT. Eleven individual-level data fields on demographics and disease dynamics were observed for non-response. The data contains 106,522 positive cases across 29 ZIP Codes.Automated CICT reduced overall collected information by 29 percentage points (SE = 0.08,Automated CICT can serve as a useful alternative to manual CICT, with no substantial evidence of skewing data along racial or ethnic lines, but manual CICT improves completeness of key data for monitoring epidemiologic patterns.This research was supported in part by the Stanford Office of Community Engagement and the Stanford Institute for Human-Centered Artificial Intelligence.

The failure to appear (FTA) for a scheduled court hearing can have serious consequences for a criminal defendant. Many have speculated that transportation is a material barrier to court appearance. We provide evidence from the first randomized controlled trial of transportation subsidies to reduce FTAs, conducted jointly with public defenders and the transportation authority in Seattle, Washington. The most intensive intervention was a transit card providing 2-3 months of free public transportation. We find little evidence that transportation subsidies reduce FTAs. The treatment group had a similar FTA rate as the control group, and we can rule out with 95% confidence that transit subsidies reduced the FTA rate by more than six percentage points. While transportation subsidies might be more effective in reducing FTAs if combined with other supportive services or outreach, our results suggest that transportation subsidies alone have limited benefits for this aspect of criminal justice.

Aims: Cardiometabolic risk factors often cluster and their coexistence increases cardiovascular disease (CVD) risk. Unraveling molecular mechanisms for the clustering of risk factors and their links to CVD may highlight promising therapeutic approaches. Methods and Results: We performed a multi-stage integrative genomic study of 71 CVD-related proteins in relation to ten CVD risk factors including lipids, adiposity, dysglycemia and blood pressure traits. Leveraging 16,000 protein expression quantitative trait loci (pQTL) with GWAS signals for CVD risk factors, we identified pQTLs on Chromosome 2 (GCKR region), 9 (ABO region), and 12 (SH2B3 region) that colocalized with at least three risk factors. By integrating pQTLs with tissue specific expression quantitative trait loci (eQTLs), we identified complex three-way regulatory relations of proteins with CVD risk factors via expression of transcripts that were distant (trans from the protein coding gene, including the trans-regulation of APOB (protein) in relation to LDL cholesterol (risk factor) through SORT1 (transcript) expression and ANGPTL3 in relation to triglycerides through PCSK7 expression. Using Mendelian randomization, we identified 13 putatively causal proteins for seven risk factors and found three circulating proteins (ANGPTL13, APOB, and NTproBNP) whose levels were associated with the corresponding risk factor when tested cross-sectionally and longitudinally in ~7000 Framingham Heart Study participants. Conclusions: Results of this integrative approach may provide a broader view of network biology underlying cardiometabolic risk and highlight promising therapeutic targets for prevention and treatment of CVD.

While the volume of adjudication by federal agencies far outstrips the volume of cases decided by the federal judiciary, researchers have devoted relatively little attention to agency adjudication and political control thereof. We study three mechanisms of presidential control of immigration adjudication: capacity-building, selection, and precedential rulemaking. First, consistent with work on bureaucratic capacity, the Trump administration achieved its goal of increasing removals of noncitizens through an unprecedented increase in total hiring of immigration judges. Second, contrary to expectations from the literatures on judicial behavior and bureaucratic politics, we find little evidence of partisan effects in immigration judge selection. Third, we demonstrate the substantial power of what we call “precedential rulemaking” – the power by the Attorney General to select cases in which to issue binding precedent. These results illustrate the importance of incorporating mechanisms of supervisory and legal control into the study of administrative courts.

Artificial intelligence (AI) promises to transform how government agencies do their work. Rapid developments in AI have the potential to reduce the cost of core governance functions, improve the quality of decisions, and unleash the power of administrative data, thereby making government performance more efficient and effective. Agencies that use AI to realize these gains will also confront important questions about the proper design of algorithms and user interfaces, the respective scope of human and machine decision-making, the boundaries between public actions and private contracting, their own capacity to learn over time using AI, and whether the use of AI is even permitted. These are important issues for public debate and academic inquiry. Yet little is known about how agencies are currently using AI systems beyond a few headline-grabbing examples or surface-level descriptions. Moreover, even amidst growing public and scholarly discussion about how society might regulate government use of AI, little attention has been devoted to how agencies acquire such tools in the first place or oversee their use. In an effort to fill these gaps, the Administrative Conference of the United States (ACUS) commissioned this report from researchers at Stanford University and New York University. The research team included a diverse set of lawyers, law students, computer scientists, and social scientists with the capacity to analyze these cutting-edge issues from technical, legal, and policy angles. The resulting report offers three cuts at federal agency use of AI: (i) a rigorous canvass of AI use at the 142 most significant federal departments, agencies, and sub-agencies (Part I) (ii) a series of in-depth but accessible case studies of specific AI applications at eight leading agencies (SEC, CPB, SSA, USPTO, FDA, FCC, CFPB, USPS) covering a range of governance tasks (Part II); and (iii) a set of cross-cutting analyses of the institutional, legal, and policy challenges raised by agency use of AI (Part III).

While the standing doctrine is one of the most widely theorized and criticized doctrines in U.S. law, its origins remain controversial. One revisionist view espouses that New Deal progressive justices purposely invented the standing doctrine to insulate administrative agencies from judicial review. Yet existing support for this “insulation thesis” is weak. Our Article provides the first systematic empirical evidence of the historical evolution of standing. We synthesize the theory and claims underlying the insulation thesis and compile a new database of every standing issue decided, along with all contested merits votes, by the Supreme Court from 1921-2006. To overcome conventional problems of haphazard case selection, we amass, read, and classify over 1,500 cases cited in historical treatments of the doctrine, assembling a database of all standing issues contested. With modern statistical methods and this new dataset – comprised of 47,570 votes for 5,497 unique issues and 229 standing issues – we find compelling evidence for the insulation thesis. Before 1940, progressive justices disproportionately deny standing to plaintiffs in cases that largely involve challenges to administrative agencies. After 1940, the political valence of the standing doctrine reverses: progressives uniformly favor standing. Justices Douglas and Black, in particular, track this evolution (and valence reversal) of the standing doctrine. Our results challenge legal inquiries of what claims are traditionally amenable to judicial resolution and highlight the unintended consequences of judicial innovation.

One of the central predicates of legal regulation of media ownership is that ownership consolidation reduces substantive viewpoint diversity. Appellate courts and in turn the Federal Communications Commission have increasingly demanded evidence for this convergence hypothesis, but extant empirical measures of viewpoint diversity sidestep the problem, ignoring diversity, viewpoints, or both. Our article develops and offers a finely-tuned, time-varying statistical measure of editorial viewpoint diversity, based on a new database of over 1600 editorials in 25 top papers from 1988-2004. Using this new measure, we assess the validity of the convergence hypothesis by examining the evolution of editorial viewpoints over the course of five major mergers and acquisitions. Our data reveals complex patterns that defy extant accounts, showing stability, convergence and divergence of viewpoints in the face of - and depending on the circumstances of - consolidation. These findings fundamentally challenge extant empirical regulatory assumptions - pointing to the crucial role of editorial policies - and deeply inform the viability of the ownership regulations and the interpretation of the 1996 Telecommunications Act.

Although central to understanding the role of the media, few quantitative measures of the political positions of media exist. Collecting and classifying editorials adopted by 23 major U.S. newspapers on 495 Supreme Court cases from 1994-2004, we apply an item response theoretic approach to place newspapers on a substantively meaningful - and long validated - scale of political preferences. Our results provide significant insights into the study of the media. We show that 17 of the 23 papers are more likely to the left of the median Justice for this period, but also find considerable evidence that this may be an artifact of the liberalness of urban, elite, high circulation papers.

Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research that investigates the effects of ballot format on voting. Using a new dataset collected by the New York Times, we investigate the causal effect of being listed on the first ballot page in the 2003 California gubernatorial recall election. California law mandates a complex randomization procedure of ballot order that approximates a classical randomized experiment in a real world setting. The recall election also poses particular statistical challenges with an unprecedented 135 candidates running for the office. We apply (nonparametric) randomization inference based on Fisher's exact test, which incorporates the complex randomization procedure and yields accurate confidence intervals. Conventional asymptotic model-based inferences are found to be highly sensitive to assumptions and model specification. Randomization inference suggests that roughly half of the candidates gained more votes when listed on the first page of the ballot.

How does partisan regulation of political markets affect elections? We investigate how the partisan control of ballot format, which is expressly regulated - often to the apparent advantage of incumbents and major parties - in all U.S. states, affects voting. Through the analysis of a unique natural experiment, we focus specifically on the longstanding question of whether the name order of candidates on ballots affects election outcomes. Since 1975, California law has mandated randomizing the ballot order with a lottery. Previous studies, relying overwhelmingly on observational data, have yielded largely conflicting results. Using improved statistical methods, our analysis of statewide elections from 1978 to 2002 reveals that ballot order might have changed the winner in twelve percent of all primary races, including major and minor party races. We propose that all electoral jurisdictions should randomize ballot order to minimize ballot effects, and show that randomization may be substantially more cost-effective at reducing voting bias than currently proposed voting technology reforms.