Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
Deep learning models have achieved high performance across different domains, such as
medical decision-making, autonomous vehicles, decision support systems, among many …
medical decision-making, autonomous vehicles, decision support systems, among many …
Artificial Intelligence and Infectious Disease Imaging
The mass production of the graphics processing unit and the coronavirus disease 2019
(COVID-19) pandemic have provided the means and the motivation, respectively, for rapid …
(COVID-19) pandemic have provided the means and the motivation, respectively, for rapid …
[HTML][HTML] Estimating causal effects with optimization-based methods: A review and empirical comparison
In the absence of randomized controlled and natural experiments, it is necessary to balance
the distributions of (observable) covariates of the treated and control groups in order to …
the distributions of (observable) covariates of the treated and control groups in order to …
FLAME: A fast large-scale almost matching exactly approach to causal inference
A classical problem in causal inference is that of matching, where treatment units need to be
matched to control units based on covariate information. In this work, we propose a method …
matched to control units based on covariate information. In this work, we propose a method …
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference
T Seale-Carlisle, S Jain, C Lee, C Levenson… - Proceedings of the …, 2024 - ojs.aaai.org
After a person is arrested and charged with a crime, they may be released on bail and
required to participate in a community supervision program while awaiting trial. These'pre …
required to participate in a community supervision program while awaiting trial. These'pre …
Causality-driven Testing of Autonomous Driving Systems
Testing Autonomous Driving Systems (ADS) is essential for safe development of self-driving
cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested …
cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested …
The secrets of machine learning: Ten things you wish you had known earlier to be more effective at data analysis
Despite the widespread usage of machine learning throughout organizations, there are
some key principles that are commonly missed. In particular,(1) there are at least four main …
some key principles that are commonly missed. In particular,(1) there are at least four main …
Causal inference in data analysis with applications to fairness and explanations
Causal inference is a fundamental concept that goes beyond simple correlation and model-
based prediction analysis, and is highly relevant in domains such as health, medicine, and …
based prediction analysis, and is highly relevant in domains such as health, medicine, and …
Malts: Matching after learning to stretch
We introduce a flexible framework that produces high-quality almost-exact matches for
causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to …
causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to …