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pro vyhledávání: '"Watson, David S."'
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
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
Autor:
Watson, David S.
High-throughput technologies such as next generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced sta
Externí odkaz:
http://arxiv.org/abs/2110.03063
Autor:
Watson, David S.
Publikováno v:
2022 ACM Conference on Fairness, Accountability, and Transparency
Explaining the predictions of opaque machine learning algorithms is an important and challenging task, especially as complex models are increasingly used to assist in high-stakes decisions such as those arising in healthcare and finance. Most popular
Externí odkaz:
http://arxiv.org/abs/2106.10191
Publikováno v:
International Conference on Machine Learning 2021
We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we
Externí odkaz:
http://arxiv.org/abs/2106.05074