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Causality and missing data group meeting | Juha Karvanen - « Causal inference with multiple incomplete data sources »

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Causality and missing data group meeting | Juha Karvanen - « Causal inference with multiple incomplete data sources »

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URL: https://webconf.math.cnrs.fr/b/imk-rmu-r3h
Date de l'événement (intitulé)
14 May 2020 - 17.00
Chapo
The group meeting on casualty and missing data, organised by the Centre de Mathématiques Appliquées (CMAP) of École polytechnique, will take place as a webinar.
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Juha Karvanen (University of Jyväskylä, Department of Mathematics and Statistics), will lead this webinar on the topic « Causal inference with multiple incomplete data sources ».

 

Assume that you have conducted an experiment where the effect of A on B is studied. An earlier study indicates that B affects C. Can we combine these data sources to estimate the causal effect of A on C?  What if we have only observational data on A and B?  

In this talk, I will present a tool for causal effect identification from multiple data sources. The data sources may suffer from selection bias and missing data. The do-search algorithm (implemented in the R package dosearch) can solve a wide range of causal and non-causal identifiability problems. The examples presented include combining observations and experiments, identification in missing not at random scenarios and causal inference under the case-control design. In an illustration, data from NHANES 2013-2016 surveys and results from a published meta-analysis are combined to estimate the reduction in average systolic blood pressure under an intervention where the use of table salt is discontinued.  

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The webinar will take place on Thursday 14 May at 17.00.

It will be broadcasted on the platform BigBlueButton. To participate, click here.

Events on the same topic
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Catégorie
Other seminars
Date de l'événement (intitulé)
October 14, 2022 | 11am
Chapo
The CEA is organizing a webinar on Friday, October 14, at 11:00 a.m., hosted by Romain LOISEAU, a doctoral student at the École des Ponts, who will present the use of Deep Learning to process 3D data, particularly from LiDAR sequences.