missing-values

ICML workshop: The Art of Learning with Missing Values

We are very pleased to announce a workshop at this year's ICML conference on The Art of Learning with Missing Values, co-organized by Julie Josse and Gaël Varoquaux.

Treatment effect estimation with missing attributes

Postponed to 2021 due to COVID-19.

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

TBA.

Causal inference with missing attributes - Application in major trauma management

TBA.

Doubly-robust treatment effect estimation

TBA.

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 …