On 18 June, at 3:00 p.m. in the Novo Banco Lecture theatre, Professor Mark Steel will present a research seminar as part of the Research Seminars in Applied Mathematics for Economics and Management, organised by CEMAPRE.
The session will be presented by Professor Mark Steel and will be the ninth seminar in the series Research Seminars in Applied Mathematics for Economics and Management.
Mark Steel is Professor of Statistics at the University of Warwick, where he was Head of the Department of Statistics from 2014 until 2018. Mark is interested in theoretical and applied Bayesian Statistics and Econometrics. He works on a variety of topics, for example, currently his main focus is on Bayesian model averaging, causal modelling and inference in models with latent variables. So far, he has published over 100 papers in international journals and leading machine learning proceedings, which have collected over 14,600 citations (Google Scholar).
Mark Steel recently served as the Editor-in-Chief of the journal Bayesian Analysis and has previously been part of editorial boards of other leading journals in Statistics and Econometrics, such as the Journal of the Royal Statistical Society, Series B, Journal of Business and Economic Statistics and the Journal of Econometrics. He has performed a variety of roles in the International Society for Bayesian Analysis (ISBA) and in the Royal Statistical Society.
Room: Novo Banco Lecture theatre, 4th floor, Quelhas Building 6
Time: 15.00
All interested parties are invited to attend and participate in the session.
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Inference on counterfactual distributions using martingale posteriors
(in collaboration with Gregor Steiner)
Abstract:
Causal inference often focuses on average effects, which can obscure important aspects of the effect distributions. Here we consider the entire posterior effects distribution by estimating full counterfactual outcome distributions. We propose a methodology for inference on counterfactual distributions that builds upon the martingale posterior framework of Fong et al. (JRSS, B, 2023). This provides a highly flexible approach to estimating densities, distribution functions, and derived quantities such as quantiles, which coherently quantifies the epistemic uncertainty regarding any target estimand of interest. Since the predictive recursions are based on an underlying nonparametric model (a Dirichlet process mixture model), it naturally inherits robustness with respect to restrictive parametric assumptions. This approach can be applied to marginal or conditional counterfactual distributions and is easily extended to an instrumental variables setup. We illustrate our approach using two well-known datasets: one investigating the impact of vitamin A supplementation on children’s survival rates with one-sided noncompliance (analyzed in Imbens and Rubin, Annals of Statistics, 1997) and another on the effect of job training (LaLonde, AER, 1986).