SAMSI - Missing data working group

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.

Treatment effect estimation with missing attributes

Interpretable ML: insights from a causal inference perspective


Causal inference with missing attributes - Application in major trauma management


Doubly-robust treatment effect estimation


New paper: R-miss-tastic: a unified platform for missing values methods and workflows

Our new paper presents 'R-miss-tastic', a platform that aims to provide an overview of standard missing values problems, methods, how to handle them in analyses, and relevant implementations of methodologies. The objective is not only to collect, but also comprehensively organize materials, to create standard analysis workflows, and to unify the community.

R-miss-tastic: a unified platform for missing values methods and workflows

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of …

Doubly-robust treatment effect estimation with incomplete confounders

The problem of missing values in causal inference has long been ignored and only recently gained some attention due to the non-negligible impacts in terms of bias induced by complete case analyses and misspecified imputation models. We discuss …

Paper: Doubly robust treatment effect estimation with missing attributes

Proposal of consistent doubly robust treatment effect estimators handling missing attributes (missing values in the covariates).

Causal Inference with critical care data

In healthcare and social sciences research, prospective observational studies are frequent, relatively easily put in place (compared to experimental randomized trial studies for instance) and can allow for different kinds of posterior analyses such …