Causal machine learning for healthcare and precision medicine

P Sanchez, JP Voisey, T Xia… - Royal Society …, 2022 - royalsocietypublishing.org
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

A tutorial on kernel density estimation and recent advances

YC Chen - Biostatistics & Epidemiology, 2017 - Taylor & Francis
This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent
advances regarding confidence bands and geometric/topological features. We begin with a …

Causal inference for time series analysis: Problems, methods and evaluation

R Moraffah, P Sheth, M Karami, A Bhattacharya… - … and Information Systems, 2021 - Springer
Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

AH Karimi, J Von Kügelgen… - Advances in neural …, 2020 - proceedings.neurips.cc
Recent work has discussed the limitations of counterfactual explanations to recommend
actions for algorithmic recourse, and argued for the need of taking causal relationships …

Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence

MC Knaus, M Lechner… - The Econometrics Journal, 2021 - academic.oup.com
We investigate the finite-sample performance of causal machine learning estimators for
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …

Causality-based neural network repair

B Sun, J Sun, LH Pham, J Shi - … of the 44th International Conference on …, 2022 - dl.acm.org
Neural networks have had discernible achievements in a wide range of applications. The
wide-spread adoption also raises the concern of their dependability and reliability. Similar to …

On feature collapse and deep kernel learning for single forward pass uncertainty

J Van Amersfoort, L Smith, A Jesson, O Key… - arXiv preprint arXiv …, 2021 - arxiv.org
Inducing point Gaussian process approximations are often considered a gold standard in
uncertainty estimation since they retain many of the properties of the exact GP and scale to …

Debiased machine learning of conditional average treatment effects and other causal functions

V Semenova, V Chernozhukov - The Econometrics Journal, 2021 - academic.oup.com
This paper provides estimation and inference methods for the best linear predictor
(approximation) of a structural function, such as conditional average structural and treatment …

Estimation of conditional average treatment effects with high-dimensional data

Q Fan, YC Hsu, RP Lieli, Y Zhang - Journal of Business & …, 2022 - Taylor & Francis
Given the unconfoundedness assumption, we propose new nonparametric estimators for the
reduced dimensional conditional average treatment effect (CATE) function. In the first stage …

Generalization bounds and representation learning for estimation of potential outcomes and causal effects

FD Johansson, U Shalit, N Kallus, D Sontag - Journal of Machine Learning …, 2022 - jmlr.org
Practitioners in diverse fields such as healthcare, economics and education are eager to
apply machine learning to improve decision making. The cost and impracticality of …