Causal fairness analysis: a causal toolkit for fair machine learning
D Plečko, E Bareinboim - Foundations and Trends® in …, 2024 - nowpublishers.com
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 …
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
Causal fairness analysis
D Plecko, E Bareinboim - arXiv preprint arXiv:2207.11385, 2022 - arxiv.org
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 …
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
Trustworthy policy learning under the counterfactual no-harm criterion
Trustworthy policy learning has significant importance in making reliable and harmless
treatment decisions for individuals. Previous policy learning approaches aim at the well …
treatment decisions for individuals. Previous policy learning approaches aim at the well …
Principal fairness for human and algorithmic decision-making
Using the concept of principal stratification from the causal inference literature, we introduce
a new notion of fairness, called principal fairness, for human and algorithmic decision …
a new notion of fairness, called principal fairness, for human and algorithmic decision …
Experimental evaluation of algorithm-assisted human decision-making: Application to pretrial public safety assessment
Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-
day lives, humans still make consequential decisions. While the existing literature focuses …
day lives, humans still make consequential decisions. While the existing literature focuses …
Bayesian safe policy learning with chance constrained optimization: Application to military security assessment during the vietnam war
Algorithmic and data-driven decisions and recommendations are commonly used in high-
stakes decision-making settings such as criminal justice, medicine, and public policy. We …
stakes decision-making settings such as criminal justice, medicine, and public policy. We …
Towards representation learning for weighting problems in design-based causal inference
Reweighting a distribution to minimize a distance to a target distribution is a powerful and
flexible strategy for estimating a wide range of causal effects, but can be challenging in …
flexible strategy for estimating a wide range of causal effects, but can be challenging in …
Starting small: Prioritizing safety over efficacy in randomized experiments using the exact finite sample likelihood
N Christy, AE Kowalski - arXiv preprint arXiv:2407.18206, 2024 - arxiv.org
We use the exact finite sample likelihood and statistical decision theory to answer questions
of``why?''and``what should you have done?''using data from randomized experiments and a …
of``why?''and``what should you have done?''using data from randomized experiments and a …
Quantifying Individual Risk for Binary Outcome: Bounds and Inference
Understanding treatment heterogeneity is crucial for reliable decision-making in treatment
evaluation and selection. While the conditional average treatment effect (CATE) is …
evaluation and selection. While the conditional average treatment effect (CATE) is …
Robust Bayes Treatment Choice with Partial Identification
We study a class of binary treatment choice problems with partial identification, through the
lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior …
lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior …