[gov3009-l] Applied Stats - 3/11 - James Robins

Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *James Robins*, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology in the Departments of Epidemiology and Biostatistics at Harvard University. He will be presenting work entitled *The Foundations of Statistics and Its Implications for Current Methods for Causal Inference from Observational and Randomized Trial Data*. Please find the abstract below and on the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/james-robins-harvard> . As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided. See you all there! -- Anton Title: The Foundations of Statistics and Its Implications for Current Methods for Causal Inference from Observational and Randomized Trial Data Abstract: The foundations of statistics are the fundamental conceptual principles that underlie statistical methodology and distinguish statistics from the highly related fields of probability and mathematics. Examples of foundational concepts include ancillarity, the conditionality principle, the likelihood principle, statistical decision theory, the weak and strong repeated sampling principle, coherence and even the meaning of probability itself. In the 1950s and 1960s, the study of the foundations of statistics held an important place in the field. However its central role faded with the revolution in computing that offered the ability to actually do more than just philosophize about how to analyze complex high dimensional data. I discuss how these principles both inform and are informed by modern approaches to causal analysis. Among other examples, I discuss from a foundational perspective are (i) methods for model and/or covariate selection including the issue of whether detailed balance on covariates is needed after one stratifies on the true or estimated propensity, (ii) the conflict between the minimization of MSE versus accuracy of confidence intervals as inferential goals and (iii) the question of a whether principled Baysesian inference must ignore the propensity score even when it is known.