Causal effects from observational studies: Inverse Probability of Treatment Weights – ONLINE

Causal effects from observational studies: Inverse Probability of Treatment Weights – ONLINE

  • 20/04/2022
    1:00 pm - 2:30 pm

Course details

statistics seminar | level: intermediate | register now
for questions related to this event, contact ugent@flames-statistics.com
affiliation: Ghent University


Abstract

The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Rather than matching, subjects can be weighted by the inverse of the propensity score which results to a pseudo-population in which treatment assignment is independent of measured baseline covariates. This allows
one to obtain unbiased estimates of average treatment effects. In this seminar, we will briefly introduce some concepts and go on to estimate weights and assess covariate balance after weighting using standardized mean differences. We are going to investigate the distribution of weights and introduce some ways of handling large
weights. We are also going to touch on Marginal Structural models and Doubly robust estimators. We will wrap up with an example using the ipw package in the R software




Prerequisites


Background readings

Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661–3679.


Fee

FREE


Venue

Ghent University-ONLINE


Instructor

dr Emmanuel Abatih


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