Prof. Dr. Daniel Scharfstein, Professor of Biostatistics, Department of Population Health Sciences, University of Utah School of Medicine
Title: “Semiparametric Sensitivity Analysis: Unmeasured Confounding in Observational Studies”
Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We build on the work of Franks et al. (2019) and Robins et al. (2000) by specifying non-identified sensitivity parameters that govern a contrast between the conditional (on measured covariates) distributions of the outcome under treatment (control) between treated and untreated individuals. We use semi-parametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We utilize this influence function to construct a one-step, split-sample bias-corrected estimator of the ACE. Our estimator depends on semi-parametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish that our estimator has $\sqrt{n}$ asymptotics. We utilize our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study. This is joint work with Razieh Nabi, Edward Kennedy, Ming-Yueh Huang, Matteo Bonvini and Marcela Smid.
Prof. Dr. Daniel Scharfstein ist Professor in der Abteilung Biostatistik der Johns Hopkins Bloomberg School of Public Health. Zudem nahm er vor kurzem eine Position als Professor für Biostatistik im Department of Population Health Sciences an der University of Utah School of Medicine an. Seine international viel beachtete Forschung befasst sich mit statistischen Methoden der kausalen Inferenz insbesondere im Zusammenhang mit Selektionsbias. Er hat wichtige Beiträge zu der Frage geleistet, wie in RCTs mit informativen fehlenden Werten bzw. Zensierung umzugehen ist, sowie zur statistischen Analyse von Beobachtungsstudien mit nicht-randomisierter Behandlung. Seine über 100 zumeist methodischen Publikationen sind in hochrangigen Journals wie z.B. JRSS, JASA, Biometrika, Statistics in Medicine erschienen. Er war Mitglied des National Academy Ausschusses, der die Empfehlung “The Prevention and Treatment of Missing Data in Clinical Trials“ verfasste. Zudem war und ist er der leitende Statistiker bei einer Vielzahl von Evaluationsstudien, z.B. der National Study of the Costs and Outcomes of Trauma (NSCOT), Guided Care for Chronically Ill Older Adults and Healthy Steps for Young Children. Zu der Debatte um das ICH E9 Addendum über “Estimands” hat er wichtige Beiträge geleistet, u.a. im Journal „Clinical Trials“.
Prof. Chris Holmes, Department of Statistics, University of Oxford.
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Thema: „Machine Learning in Biometry“
Machine learning (ML) and artificial intelligence (AI) have had a major impact across many disciplines including biometrics. In the first half of this talk we will review some of the characteristics of ML that make for successful applications and also those features that present challenges, in particular around robustness and reproducibility. Relatively speaking, ML is mainly concerned with prediction while the majority of biometric analyses are focussed on inference. In the second half of the talk we will review the prediction-inference dichotomy and explore, from a Bayesian perspective, the theoretical foundations on how modern ML predictive models can be utilised for inference.