We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of covariates from observational data. Available methods such as inverse propensity weighting are not …
Causal survival analysis consists in estimating the effect of a treatment on time-to-event outcome(s). We focus on estimating the restricted mean survival time (RMST), the average survival time from baseline to a pre-specified time, between treated …
Missing attributes are ubiquitous in causal inference, as they are in most applied statistical studies. In this work, we consider various sets of assumptions under which causal inference is possible despite missing attributes and discuss …
With increasing data availability, treatment causal effects can be evaluated across different datasets, both randomized trials and observational studies. Randomized trials isolate the effect of the treatment from that of unwanted (confounding) …
The simultaneous availability of experimental and observational data to estimate a treatment effect is both an opportunity and a statistical challenge: Combining the information gathered from both data is a promising avenue to build upon the internal …
We're excited to announce the Leveraging Observational Data with Machine Learning Virtual Workshop - brought to you by Michael Blum (Owkin), Julie Josse (Inria), and Raphael Porcher (Université de Paris) — on June 22nd and 23rd 2021! Register now (for free) & check out our great line-up of keynote speakers and panelists: Workshop website.
Dans le domaine de l’apprentissage machine, de grands progrès ont été réalisés dans l’obtention de modèles prédictifs puissants, mais ces modèles reposent sur des corrélations entre variables et ne permettent pas de comprendre les mécanismes …
In machine learning, there has been great progress in obtaining powerful predictive models, but these models rely on correlations between variables and do not allow for an understanding of the underlying mechanisms or how to intervene on the system …
Data integration: combining multiple data sources for causal inference & Bridging theory and practice. Combining trial and real-world evidence studies, probability and non-probability samples, etc.