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SUMMARY:Bayesian regression discontinuity design with unknown cutoff
DTSTART:20250502T141500
DTEND:20250502T151500
DTSTAMP:20260415T024312Z
UID:eec8514e48a6f12dded61fbb5fecee365ad484a40cfcbae59f2fa07e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Stéphanie van der Pas\, Amsterdam UMC\nThe regression discont
 inuity design (RDD) is a quasi-experimental approach used to estimate the 
 causal effects of an intervention assigned based on a cutoff criterion. RD
 D exploits the idea that close to the cutoff units below and above are sim
 ilar\; hence\, they can be meaningfully compared. Consequently\, the causa
 l effect can be estimated only locally at the cutoff point. This makes the
  cutoff point an essential element of RDD. However\, especially in medical
  applications\, the exact cutoff location may not always be disclosed to t
 he researcher\, and even when it is\, the actual location may deviate from
  the official one. As we illustrate on the application of RDD to the HIV t
 reatment eligibility data\, estimating the causal effect at an incorrect c
 utoff point leads to meaningless results. The method we present\, LoTTA (L
 ocal Trimmed Taylor Approximation)\, can be applied both as an estimation 
 and validation tool in RDD. We use a Bayesian approach to incorporate prio
 r knowledge and uncertainty about the cutoff location in the causal effect
  estimation. At the same time\, LoTTA is fitted globally to the whole data
 \, whereas RDD is a local\, boundary point estimation problem. In this wor
 k we address a natural question that arises: how to make Bayesian inferenc
 e more local to render a meaningful and powerful estimate of the treatment
  effect?
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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