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pro vyhledávání: '"WATSON, DAVID A."'
Instrumental variables (IVs) are widely used to estimate causal effects in the presence of unobserved confounding between exposure and outcome. An IV must affect the outcome exclusively through the exposure and be unconfounded with the outcome. We pr
Externí odkaz:
http://arxiv.org/abs/2411.06913
Publikováno v:
Minds and Machines, 2023
Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisati
Externí odkaz:
http://arxiv.org/abs/2407.05341
On the whole, the U.S. Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can b
Externí odkaz:
http://arxiv.org/abs/2407.06234
Autor:
Watson, David S., Penn, Jordan, Gunderson, Lee M., Bravo-Hermsdorff, Gecia, Mastouri, Afsaneh, Silva, Ricardo
Publikováno v:
40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the $\textit{exclusion criterion}$, which states th
Externí odkaz:
http://arxiv.org/abs/2404.04446
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast, explaining the $\textit{uncertainty}$ of model outputs has received re
Externí odkaz:
http://arxiv.org/abs/2306.05724
One of the goals of causal inference is to generalize from past experiments and observational data to novel conditions. While it is in principle possible to eventually learn a mapping from a novel experimental condition to an outcome of interest, pro
Externí odkaz:
http://arxiv.org/abs/2306.04027
Publikováno v:
AStA Advances in Statistical Analysis (2023)
Despite the popularity of feature importance (FI) measures in interpretable machine learning, the statistical adequacy of these methods is rarely discussed. From a statistical perspective, a major distinction is between analyzing a variable's importa
Externí odkaz:
http://arxiv.org/abs/2210.03047
Publikováno v:
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
We propose methods for density estimation and data synthesis using a novel form of unsupervised random forests. Inspired by generative adversarial networks, we implement a recursive procedure in which trees gradually learn structural properties of th
Externí odkaz:
http://arxiv.org/abs/2205.09435
Autor:
Watson, David S., Silva, Ricardo
Publikováno v:
38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)
Inferring causal relationships from observational data is rarely straightforward, but the problem is especially difficult in high dimensions. For these applications, causal discovery algorithms typically require parametric restrictions or extreme spa
Externí odkaz:
http://arxiv.org/abs/2205.05715
Publikováno v:
In Healthcare Analytics December 2024 6
