Virtual BSU Seminar: 'Parameterizing Causal Marginal Models'

Add to your list(s) Download to your calendar using vCal

• Prof Robin Evans, University of Oxford
• Tuesday 16 June 2020, 14:00-15:00
• Virtual Seminar .

If you have a question about this talk, please contact Alison Quenault.

If you would like to join this virtual seminar, please email: research_admin@mrc-bsu.cam.ac.uk for more information.

Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the quantity of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified.

Naive attempts to specify a model parametrically can lead to unwanted consequences such as incompatible parametric assumptions or the so-called ‘g-null paradox’. As a consequence it is difficult to perform likelihood-based inference, or even to simulate from the model in a general way. We argue that this occurs because insufficient thought is given to which parts of the model are truly of interest, what nuisance parameters remain to be specified given the parts of interest, and what other aspects of the distribution are redundant and can be derived from the rest. We provide a recipe for constructing a smooth, non-redundant parameterization using (identifiable) causal quantities of interest.

We adapt some existing marginal parameterizations to causal models for both continuous and discrete data, allowing us to parameterize a wide range of causal models including marginal structural models (MSMs), Cox MSMs and structural nested models. This makes it easy to simulate from and fit models; to introduce possibly high-dimensional individual-level covariates; and to include additional assumptions such as stationarity or symmetry. Our approach is likelihood-based, and therefore also amenable to a fully Bayesian approach. We also provide an R package that implements these routines.

This talk is part of the MRC Biostatistics Unit Seminars series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• BHRU Annual Lecture 2015
• BHRU Annual Lecture 2016
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Immunology
• Cambridge talks
• Cardiovascular Epidemiology Unit Special Seminars
• Chris Davis' list
• Department of Public Health and Primary Care
• Graduate-Seminars
• Interested Talks
• Life Sciences
• Life Sciences
• ME Seminar
• MRC Biostatistics Unit Seminars
• Primary Care
• PublicHealth@Cambridge
• Trust & Technology Initiative - interesting events
• Virtual Seminar
• bld31
• my_list
• ndk22's list
• ob366-ai4er
• other talks
• primary care
• rp587

Note that ex-directory lists are not shown.