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UID:184@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20260210T140000
DTEND;TZID=Europe/Paris:20260210T140000
DTSTAMP:20260825T130330Z
URL:https://isdm.umontpellier.fr/events/causal-inference-from-longitudinal
 -data-latent-adjustment-variables-and-counterfactual-prediction/
SUMMARY:Causal Inference from Longitudinal Data: Latent Adjustment Variable
 s and Counterfactual Prediction
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Myriam Tami (Univ. Paris-Saclay
  / CentraleSupélec)\n\nThe presented works focus on causal inference from
  longitudinal data\, with two complementary axis. The first addresses sett
 ings in which some adjustment variables are latent or unobserved\, while t
 he second targets the estimation of long-term treatment effects. The first
  contribution investigates how to account for unobserved adjustment variab
 les using a probabilistic generative model. We propose a causal dynamic va
 riational autoencoder\, which learns a latent representation intended to s
 ubstitute for the missing variables. This approach relies on a conditional
  Markov assumption linking the latent space to the unobserved variables. T
 he model is trained by maximizing a regularized conditional likelihood\, i
 n order to handle imbalances in the representation space and to enhance th
 e stability of temporal inference. The second contribution aims at improvi
 ng long-term counterfactual prediction. The goal is to learn a compact lat
 ent representation capable of summarizing the relevant information from th
 e historical data to predict outcomes under a given hypothetical treatment
 . A contrastive approach is used to capture long-term dependencies\, and i
 s further regularized to ensure invertibility of the latent representation
  and preservation of confounding and static information\, using mutual inf
 ormation criteria. Selection bias is also addressed by minimizing the depe
 ndence between the current representation and the future treatment assignm
 ent. The proposed methods are evaluated on both synthetic and real-world d
 atasets.\n\nMachine Learning in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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 ier\, 34000\, France;X-APPLE-RADIUS=100;X-TITLE=Room 02.124 Building 5:geo
 :0,0
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DTSTART:20251026T020000
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