Debiased machine learning estimators for nonparametric inference of smooth functionals of
the data-generating distribution can suffer from excessive variability and instability. For this …

Causal inference has been increasingly reliant on observational studies with rich covariate
information. To build tractable causal procedures, such as the doubly robust estimators, it is …

Algorithms for constraint-based causal discovery select graphical causal models among a
space of possible candidates (eg, all directed acyclic graphs) by executing a sequence of …

The calculation of the Augmented Inverse Probability Weighting (AIPW) estimator of the
Average Treatment Effect (ATE) is carried out in two steps, where in the first step, the …

A common view in epidemiology is that automated confounder selection methods, such as
backward elimination, should be avoided as they can lead to biased effect estimates and …

Convolutional neural networks (CNN) have been successful in machine learning
applications. Their success relies on their ability to consider space invariant local features …

Hazard ratios are frequently reported in time-to-event and epidemiological studies to assess
treatment effects. In observational studies, the combination of propensity score weights with …

Confounding bias and selection bias are two significant challenges to the validity of
conclusions drawn from applied causal inference. The latter can arise through informative …

Various methods have recently been proposed to estimate causal effects with confidence
intervals that are uniformly valid over a set of data generating processes when high …

Semiparametric inference on average causal effects from observational data is based on
assumptions yielding identification of the effects. In practice, several distinct identifying …