I work on
- Statistical foundations of causal inference: graphical models, semiparametric theory, robustness vs. efficiency trade-off
and, largely motivated by problems from causality, also
- Honest & flexible uncertainty quantification: model selection, irregularity, finite sample analysis, handling preprocessed data, randomization and derandomization, Bayes and empirical Bayes.
|Jan 9, 2023||New preprint on how to handle multiple data splitting and exchangeable p-values.|
|Dec 17, 2022||Presenting my work on inference for multiple data splitting and exchangeable p-values in CMStatistics 2022 at King’s College London.|
|Oct 31, 2022||Paper Variable elimination, graph reduction and efficient g-formula is accepted to Biometrika.|
|Aug 30, 2022||A new review paper that clarifies misconceptions on confounder selection.|