causal-inference

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.

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 …

Causal Inference with Incomplete Confounders

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 …