Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIG
Durée : 12 months
Contact : Emilie.Devijver@univ-grenoble-alpes.fr
Date limite de publication : 2022-09-18

Contexte :
Causality plays a central role in science and has been the subject of many debates among philosophers, biologists, mathematicians and physicists, to name but a few. The recent decades have seen the development, from philosophers, mathematicians, and computer scientists, of different models and methods to infer causal relations from data and to reason on the basis of these relations. If the first studies were dedicated to non temporal data, more and more studies now focus on time series. Indeed, time series arise as soon as observations, from sensors or experiments, for example, are collected over time. 

Despite the importance of time series, very few works (apart from [6]) have studied methods to estimate the causal effect of interventions. This project will focus on this through the steps described below.

Sujet :
– Literature review on the estimation of causal effects [1,2] as well as treatment effects in time series [3,4,5].
– Design of an estimator for back-door probabilities
– Generalization to other identification probabilities (as in the ID algorithm).
– Theoretical study of the corresponding estimator. 
 
The scientific orientations of the post-doc may vary according to the candidate’s background and interests.

Profil du candidat :
Programming skills: proficiency in R or Python.
Proficiency in either French or English.

Formation et compétences requises :
PhD in machine learning or statistics.

Adresse d’emploi :
Bâtiment IMAG
Grenoble

Document attaché : 202207111449_postdoc_causalReasoning.pdf