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Optimal Causal Imputation for Control

Dong, Roy and Mazumdar, Eric and Sastry, S. Shankar (2017) Optimal Causal Imputation for Control. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210903-213646411

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Abstract

The widespread applicability of analytics in cyber-physical systems has motivated research into causal inference methods. Predictive estimators are not sufficient when analytics are used for decision making; rather, the flow of causal effects must be determined. Generally speaking, these methods focus on estimation of a causal structure from experimental data. In this paper, we consider the dual problem: we fix the causal structure and optimize over causal imputations to achieve desirable system behaviors for a minimal imputation cost. First, we present the optimal causal imputation problem, and then we analyze the problem in two special cases: 1) when the causal imputations can only impute to a fixed value, 2) when the causal structure has linear dynamics with additive Gaussian noise. This optimal causal imputation framework serves to bridge the gap between causal structures and control.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1703.07049arXivDiscussion Paper
ORCID:
AuthorORCID
Dong, Roy0000-0002-1815-269X
Mazumdar, Eric0000-0002-1815-269X
Record Number:CaltechAUTHORS:20210903-213646411
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210903-213646411
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110717
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Sep 2021 17:02
Last Modified:07 Sep 2021 17:02

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