1 citations · 1 across the 1 of their papers we have counts for
6 papers
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation
Rémi Khellaf, Aurélien Bellet, Julie Josse
Causal inference typically assumes centralized access to individual-level data. Yet, in practice, data are often decentralized across multiple sites, making centralization infeasib…
What Is a Good Imputation Under MAR Missingness?
Jeffrey Näf, Erwan Scornet, Julie Josse
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
Quantifying Treatment Effects: Estimating Risk Ratios in Causal Inference
Ahmed Boughdiri, Julie Josse, Erwan Scornet
Randomized Controlled Trials (RCT) are the current gold standards to empirically measure the effect of a new drug. However, they may be of limited size and resorting to complementa…
Optimal Transport with Heterogeneously Missing Data
Linus Bleistein, Aurélien Bellet, Julie Josse
We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely…
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) es…
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Houssam Zenati, Judith Abécassis, Julie Josse +1
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double…