Missing Data

Planning a method for covariate adjustment in individually randomised trials: a practical guide

April, 2022

Abstract

Background

It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them.

Methods

Causally Colored Reflections on Leo Breiman’s “Statistical Modeling: The Two Cultures”

DSA ADS Course, 2022

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss Leo Breiman’s “Statistical Modeling: The Two Cultures”  in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

July, 2021

Statistical Modelling in the Age of Data Science

DSA ADS Course, 2022

Causal Machine learning, Double Machine Learning, Targeted Learning, Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.

Discuss causal machine learning, double machine learning and targeted learning.

Statistical Modelling in the Age of Data Science - July, 2021

Abstract

Statistical Modeling The Two Cultures

DSA ADS Course, 2022

Statistical Analysis, Data Interpretation, Causal Analysis, Causality, Data Fusion, Missing Data, Counterfactuals

Discuss the two cultures of statistical modeling according to Leo Breiman in light of recent advances in machine learning and causal inference and the separation between the data-fitting and data-interpretation components of statistical modeling.