Search Results for author: David Danks

Found 6 papers, 0 papers with code

Constraint-Based Causal Structure Learning from Undersampled Graphs

no code implementations18 May 2022 Mohammadsajad Abavisani, David Danks, Sergey Plis

Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data.

Informativeness Time Series

Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models

no code implementations26 Jul 2019 Mauricio Gonzalez Soto, David Danks, Hugo J. Escalante Balderas, L. Enrique Sucar

In this work, we will show how probabilities for decision making can be grounded in causal models by considering decision problems in which the available actions and consequences are causally connected.

Causal Inference Decision Making +1

Causal Discovery from Subsampled Time Series Data by Constraint Optimization

no code implementations25 Feb 2016 Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks

This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.

Causal Discovery Time Series

Rate-Agnostic (Causal) Structure Learning

no code implementations NeurIPS 2015 Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.

Time Series

Tracking Time-varying Graphical Structure

no code implementations NeurIPS 2013 Erich Kummerfeld, David Danks

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary.

Integrating Locally Learned Causal Structures with Overlapping Variables

no code implementations NeurIPS 2008 David Danks, Clark Glymour, Robert E. Tillman

In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset.

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