Department of Applied Statistics, Social Science, and Humanities

PRIISM

• PRIISM Seminars

  The PRIISM seminar series consists of research seminars of interest to an applied statistics audience, from innovative applications of applied statistics to novel statistical theory and methodology. For an archive of past events, here (2008 - 2015) and here (2015 - 2018).  Links to related seminars are given here.
  
  Upcoming and Recent Events: Fall 2017 and Spring 2018

   

  Date, Time, Location	Speaker Name, Affiliation	Topic (click for more info)
  9/14/2017 (Thurs.), 12:30-2:00pm, 295 Lafayette Street, 2nd Floor, The Rudin Forum* (NYU Wagner)	Don Rubin (Harvard)	Embedding the Analysis of Observational Data for Causal Effects within a Hypothetical Randomized Experiment
  Abstract: Consider a statistical analysis that draws causal inferences using an observational data set, inferences that are presented as being valid in the standard frequentist senses; that is an analysis that produces (a) point estimates, which are presented as being approximately unbiased for their estimands, (b) p-values, which are presented as being valid in the sense of rejecting true null hypotheses at the nominal level or less often, and/or (c) confidence intervals, which are presented as having at least their nominal coverage for their estimands. For the hypothetical validity of these statements (that is, if certain explicit assumptions were true, then the validity of the statements would follow), the analysis must embed the observational study in a hypothetical randomized experiment that created the observed data, or a subset of that data set. This effort is a multistage effort with thought-provoking tasks, especially in the first stage, which is purely conceptual. Other stages may often rely on modern computing to implement efficiently, but the first stage demands careful scientific argumentation to make the embedding plausible to thoughtful readers of the proffered statistical analysis. Otherwise, the resulting analysis is vulnerable to criticism for being simply a presentation of scientifically meaningless arithmetic calculations. In current practice, this perspective is rarely implemented with any rigor, for example, completely eschewing the first stage. Instead, often analyses appear to be conducted using computer programs run with limited consideration of the assumptions of the methods being used, producing tables of numbers with recondite interpretations, and presented using jargon, which may be familiar but also may be scientifically impenetrable. Somewhat paradoxically, the conceptual tasks, which are usually omitted in publications, often would be the most interesting to consumers of the analyses. These points will be illustrated using the analysis of an observational data set addressing the causal effects of parental smoking on their children’s lung function. This presentation may appear provocative, but it is intended to encourage applied researchers, especially those working on problems with policy implications, to focus on important conceptual issues rather than on minor technical ones.
  10/18/17 (Weds.), 10:30am-12:00pm, 3rd Fl. Conf. Rm, Kimball	Chuck Huber (Stata Corp.)	Introduction to Bayesian Analysis Using Stata
  Abstract: Bayesian analysis has become a popular tool for many statistical applications. Yet many data analysts have little training in the theory of Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models using Stata. No prior knowledge of Bayesian analysis is necessary and specific topics will include the relationship between likelihood functions, prior, and posterior distributions, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, and how to use Stata's Bayes prefix to fit Bayesian models.
  11/3/17 (Fri.) ALL DAY (9-5, tent.), 3rd Fl. Conf. Rm, Kimball	Leading experts in SSD, Causal & Bayesian Inference	Unraveling and Anticipating Heterogeneity: Single Subject Designs & Individualized Treatment Protocols
  Abstract: This will be a 1-day symposium on the topic of Single Subject Design (SSD) and methods for their analysis.  It will bring together leading researchers in the areas of multilevel models, Bayesian modeling, and meta-analysis to discuss best practices with leading practitioners who utilize SSDs as well as how to use results from single case designs to better inform larger scale clinical trials in this field.   These practitioners will be drawn from the fields of special education and rehabilitation science.  In particular, the areas of Physical Therapy, Occupational Therapy and Communication Science Disorders will be invited. Panel discussions will be convened in which methodologists are paired with practitioners to discuss each phase of the science, from exploratory data analysis (related to designs employing graphical methods), more general design aspects, and analysis.  Particular emphasis will be given to research supporting Individualized Treatment Protocols.  In addition, there will be individual presentations representing new methodology for these designs, and reports from practitioners on their ongoing clinical trials to spur additional discussion of appropriate methodology.
  2/7/2018, (Weds.) 11:00 am - 12:00 pm  3rd Fl. Conf. Rm, Kimball	Howard Wainer (NMBE)	Graphs as Poetry
  Abstract: Visual displays of empirical information are too often thought to be just compact summaries that, at their best, can clarify a muddled situation. This is partially true, as far as it goes, but it omits the magic. We have long known that data visualization is an alchemist that can make good scientists great and transform great scientists into giants. In this talk we will see that sometimes, albeit too rarely, the combination of critical questions addressed by important data and illuminated by evocative displays can achieve a transcendent, and often wholly unexpected, result. At their best, visualizations can communicate emotions and feelings in addition to cold, hard facts.
  2/28/2018, (Weds.) 11:00 am - 12:00 pm  3rd Fl. Conf. Rm, Kimball	Keith Goldfeld (NYUMC)	Simulating a Marginal Structural Model
  Abstract: In so many ways, simulation is an extremely useful tool to learn, teach, and understand the theory and practice of statistics. A series of examples (interspersed with minimal theory) will hopefully illuminate the underbelly of confounding, colliding, and marginal structural models. Drawing on the potential outcomes framework, the examples will use the R simstudy package, a tool that is designed to make data simulation as painless as possible.
  4/25/2018, (Weds.) 11:00 am - 12:00 pm  3rd Fl. Conf. Rm, Kimball	Jennifer Hill (NYU)	BART for Causal Inference
  Abstract: There has been increasing interest in the past decade in use of machine learning tools in causal inference to help reduce reliance on parametric assumptions and allow for more accurate estimation of heterogeneous effects. This talk reviews the work in this area that capitalizes on Bayesian Additive Regression Trees, an algorithm that embeds a tree-based machine learning technique within a Bayesian framework to allow for flexible estimation and valid assessments of uncertainty. It will further describe extensions of the original work to address common issues in causal inference: lack of common support, violations of the ignorability assumption, and generalizability of results to broader populations. It will also describe existing R packages for traditional BART implementation as well as debut a new R package for causal inference using BART, bartCause.
  5/2/2018, (Weds.) 11:00 am - 12:00 pm  3rd Fl. Conf. Rm, Kimball	Alejandro Ganimian (NYU)	Disrupting Education? Experimental Evidence on Technology-Aided Instruction in India
  Abstract: We present experimental evidence on the impact of a personalized technology-aided after-school instruction program on learning outcomes. Our setting is middle-school grades in urban India, where a lottery provided winning students with a voucher to cover program costs. We find that lottery winners scored 0.36σ higher in math and 0.22σ higher in Hindi relative to lottery losers after just 4.5-months of access to the program. IV estimates suggest that attending the program for 90 days would increase math and Hindi test scores by 0.59σ and 0.36σ respectively. We find similar absolute test score gains for all students, but the relative gain was much greater for academically-weaker students because their rate of learning in the control group was close to zero. We show that the program was able to effectively cater to the very wide variation in student learning levels within a single grade by precisely targeting instruction to the level of student preparation. The program was cost effective, both in terms of productivity per dollar and unit of time. Our results suggest that well-designed technology-aided instruction programs can sharply improve productivity in delivering education.

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   ^*Seminar is sponsored by another group, but PRIISM community is invited.

  
  Links to Related Seminars:

  • Education Seminar Series
  • MaD (Math and Data)
  • CDS (Data Science) Lunch
  • IOMS/Statistics  
    
  • NLP and Text as Data Speaker Series