We have the pleasure, thanks to the support of the ESSEC IDS dpt, Institut des Actuaires, LabEx MME-DII, and the group BFA (SFdS), to invite:
Prof. Julie JOSSE
Ecole Polytechnique, Université Paris-Saclay
Visiting Researcher at Google Brain
“ Treatment effect estimation with missing attributes "
Inferring causal effects of a treatment or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference suffer when covariates have missing values, which is ubiquitous in application. This work is motivated by medical questions about different treatments based on a large prospective database. The Missing data greatly complicate causal analyses as they either require strong assumptions about the missing data generating mechanism or an adapted unconfoundedness hypothesis. In this talk, I will first provide a classification of existing methods according to the main underlying assumptions, which are based either on variants of the classical unconfoundedness assumption or relying on assumptions about the mechanism that generates the missing values. Then, I will present two recent contributions on this topic: (1) an extension of doubly robust estimators that allows handling of missing attributes, and (2) an approach to causal inference based on variational autoencoders adapted to incomplete data. I will illustrate the topic on an observational medical database which has heterogeneous data and a multilevel structure to assess the impact of the administration of a treatment on survival.
Jeremy Heng, Olga Klopp and Marie Kratz
and Riada Djebbar (Singapore Actuarial Society - ERM)