BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.4.0.1//EN TZID:Europe/Paris X-WR-TIMEZONE:Europe/Paris BEGIN:VEVENT UID:207@isdm.umontpellier.fr DTSTART;TZID=Europe/Paris:20250610T140000 DTEND;TZID=Europe/Paris:20250610T140000 DTSTAMP:20260825T132404Z URL:https://isdm.umontpellier.fr/events/principal-score-methods-for-surviv al-data/ SUMMARY:Principal score methods for survival data DESCRIPTION:Online\n\nMachine Learning in Montpellier\, Theory &\; Pract ice - Emma Torrini (Universita degli studi Firenze)\n\nThe presence of int ermediate variables that occur after treatment assignment and potentially influence the outcome complicates the estimation of causal effects\, even in randomized trials. Principal stratification is a methodology employed t o estimate the causal effect of a treatment on an outcome in the presence of such intermediate variables. This approach involves the cross-classific ation of units into latent groups\, referred to as principal strata\, whic h are defined by the potential values of the intermediate variable under t reatment and control conditions. The focus is on estimating principal caus al effects\, which are causal effects within these principal strata. The i dentification of principal causal effects can be achieved through various methods\, one of which is the principal score method. At present\, princip al score methods are available for a binary treatment\, binary intermediat e variable\, and binary/continuous/survival outcome. Motivated by a cardio vascular randomized trial\, we take an initial step towards integrating a survival intermediate variable into principal score methods.\n\nMachine Le arning in Montpellier\, Theory &\; Practice ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/ 2026/06/ml-mtp-gC78d5.png CATEGORIES:ML MTP END:VEVENT BEGIN:VTIMEZONE TZID:Europe/Paris X-LIC-LOCATION:Europe/Paris BEGIN:DAYLIGHT DTSTART:20250330T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST END:DAYLIGHT END:VTIMEZONE END:VCALENDAR