Causal effects from observational studies: Instrumental Variables – ONLINE

Causal effects from observational studies: Instrumental Variables – ONLINE

  • 22/06/2022
    1:00 pm - 2:30 pm

Course details

data science seminar | level: intermediate | register now
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affiliation: Ghent University


We have previously presented methods to mitigate the impact of measured confounding on the estimation of a
causal treatment effects such as Matching and Weighting using propensity scores. A main critic of these methods
is that they only control for measured confounders and do not control for unmeasured confounders. Instrumental
variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the
measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured
confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the
treatment. In summary, the IV method seeks to find a randomized experiment embedded in an observational
study and uses this to estimate the causal treatment effect.
This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the
assumptions; methods of estimation and sources of instrumental variables.
We will wrap up with an example that uses the ivpack package in the R software.


Background readings

Baiocchi, Michael et al. “Instrumental variable methods for causal inference.” Statistics in medicine vol. 33,13
(2014): 2297-340. doi:10.1002/sim.6128




Ghent University - ONLINE


dr Emmanuel Abatih

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