BEGIN:VCALENDAR VERSION:2.0 PRODID:-// BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:19700329T010000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:19701025T020000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:MRC Biostatistics Unit Seminars SUMMARY:Virtual BSU Seminar: 'Parameterizing Causal Margin al Models' - Prof Robin Evans\, University of Oxfo rd DTSTART;TZID=Europe/London:20200616T140000 DTEND;TZID=Europe/London:20200616T150000 UID:TALK142747AT URL: DESCRIPTION:Many statistical problems in causal inference invo lve a probability distribution other than the one from which data are actually observed\; as an addi tional complication\, the quantity of interest is often a marginal quantity of this other probabilit y distribution. This creates many practical compl ications for statistical inference\, even where th e problem is non-parametrically identified. \n\nNa ive attempts to specify a model parametrically can lead to unwanted consequences such as incompatibl e parametric assumptions or the so-called 'g-null paradox'. As a consequence it is difficult to perf orm likelihood-based inference\, or even to simula te 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 aspe cts of the distribution are redundant and can be d erived from the rest. We provide a recipe for con structing a smooth\, non-redundant parameterizatio n using (identifiable) causal quantities of intere st. \n\nWe adapt some existing marginal parameteri zations to causal models for both continuous and d iscrete data\, allowing us to parameterize a wide range of causal models including marginal structur al models (MSMs)\, Cox MSMs and structural nested models. This makes it easy to simulate from and fi t models\; to introduce possibly high-dimensional individual-level covariates\; and to include addit ional assumptions such as stationarity or symmetry . Our approach is likelihood-based\, and therefore also amenable to a fully Bayesian approach. We a lso provide an R package that implements these rou tines.\n LOCATION:Virtual Seminar CONTACT:Alison Quenault END:VEVENT END:VCALENDAR