Search Results for author: David S. Watson

Found 6 papers, 6 papers with code

Conditional Feature Importance for Mixed Data

1 code implementation6 Oct 2022 Kristin Blesch, David S. Watson, Marvin N. Wright

The CPI enables conditional FI measurement that controls for any feature dependencies by sampling valid knockoffs - hence, generating synthetic data with similar statistical properties - for the data to be analyzed.

Feature Importance Interpretable Machine Learning

Causal discovery under a confounder blanket

1 code implementation11 May 2022 David S. Watson, Ricardo Silva

Under a structural assumption called the $\textit{confounder blanket principle}$, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity.

Causal Discovery

Rational Shapley Values

1 code implementation18 Jun 2021 David S. Watson

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.

Explainable artificial intelligence

Operationalizing Complex Causes: A Pragmatic View of Mediation

1 code implementation9 Jun 2021 Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).

Testing Conditional Independence in Supervised Learning Algorithms

3 code implementations28 Jan 2019 David S. Watson, Marvin N. Wright

We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.

Association Causal Discovery +1

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