RAND Statistics Seminar Series

The RAND Statistics Group holds periodic seminars that are open to all RAND employees and members of the public. The chairs of the seminar series are Brian Vegetabile and Joshua Snoke. Members of the public who would like to attend should contact Naomi Hale in advance.

If you would like to meet with any of the speakers, or request a paper, please contact Naomi Hale.

Upcoming Seminars | Past Seminars

Upcoming Seminars

  • Statistics and the Fair Administration of Justice

    Presented by Hal Stern, University of California, Irvine

    March 25, 2020
    Time: 12:00 – 1:30 p.m. PT
    Host location: Santa Monica
    Other locations: Pittsburgh

    Abstract: Statistics has emerged as a critical topic in ongoing discussions regarding the use of science to assess forensic evidence. A 2009 National Academies report on forensic science and a subsequent 2016 report by the President’s Council of Advisers on Science and Technology raised questions about the scientific underpinnings for the analysis of a number of types of forensic evidence. Misapplication of forensic science has been identified as a contributing factor in nearly half of 362 cases in which DNA helped exonerate wrongly-convicted individuals. For these reasons there has been an increased focus on evaluating the ways in which evidence is analyzed, interpreted and reported with an eye towards providing more scientifically justified methods. There are three common approaches to the analysis and interpretation of forensic evidence: (1) forensic conclusions as expert opinion; (2) two-stage procedures (determination of similarity of known/questioned items followed by an assessment of significance); (3) likelihood ratios. The first of these, forensic conclusions as expert opinion, has been the standard approach in the pattern comparison disciplines (e.g., fingerprints, firearms, handwriting) but is now being questioned. The logical and statistical issues associated with each of the approaches are discussed in the context of current research.

  • Nonparametric causal effects based on incremental propensity score interventions

    Presented by Edward Kennedy, Carnegie Mellon University

    April 30, 2020
    Time: 10:30 a.m. – 12:00 p.m. PT
    Host location: Pittsburgh

    Most work in causal inference considers deterministic interventions that set each unit’s treatment to some fixed value. However, when some units have zero chance of being treated at that level (i.e., positivity violations) these interventions can lead to non-identification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental interventions that shift propensity score values rather than set treatments to fixed values. Incremental interventions have several crucial advantages. First, they avoid positivity assumptions entirely. Second, they require no parametric assumptions and yet still admit a simple characterization of longitudinal effects, independent of the number of timepoints. For example, they allow longitudinal effects to be visualized with a single curve instead of lists of coefficients. After characterizing incremental interventions and giving identifying conditions for corresponding effects, we also develop general efficiency theory, propose efficient nonparametric estimators that can attain fast convergence rates even when incorporating flexible machine learning, and propose a bootstrap-based confidence band and simultaneous test of no treatment effect. Finally, we explore finite-sample performance via simulation, and apply the methods to study time-varying sociological effects of incarceration on entry into marriage.

Past Seminars