Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
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 …
advances regarding confidence bands and geometric/topological features. We begin with a …
Causal inference for time series analysis: Problems, methods and evaluation
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 …
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 …
actions for algorithmic recourse, and argued for the need of taking causal relationships …
Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence
We investigate the finite-sample performance of causal machine learning estimators for
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
Causality-based neural network repair
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 …
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
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 …
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 …
(approximation) of a structural function, such as conditional average structural and treatment …
Estimation of conditional average treatment effects with high-dimensional data
Given the unconfoundedness assumption, we propose new nonparametric estimators for the
reduced dimensional conditional average treatment effect (CATE) function. In the first stage …
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
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 …
apply machine learning to improve decision making. The cost and impracticality of …