In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Purpose The targeted maximum likelihood estimation (TMLE) statistical data analysis
framework integrates machine learning, statistical theory, and statistical inference to provide …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

In recommender systems, usually the ratings of a user to most items are missing and a
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

Most causal inference methods consider counterfactual variables under interventions that
set the exposure to a fixed value. With continuous or multi-valued treatments or exposures …

The idea of covariate balance is at the core of causal inference. Inverse propensity weights
play a central role because they are the unique set of weights that balance the covariate …

In this issue, Naimi et al.(Am J Epidemiol. 2023; 192 (9): 1536–1544) discuss a critical topic
in public health and beyond: obtaining valid statistical inference when using machine …

We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement
learning using function approximation for marginal importance weights and $ q $-functions …