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Causal Discovery Under Non-Stationary Feedback

Strobl, Eric (2017) Causal Discovery Under Non-Stationary Feedback. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Causal discovery algorithms help investigators infer causal relations between random variables using observational data. In this thesis, I relax the acyclicity and stationary distribution assumptions imposed by the Fast Causal Inference (FCI) algorithm, a constraint-based causal discovery method allowing latent common causes and selection bias. I provide two major contributions in doing so. First, I introduce a representation of causal processes called Continuous time Markov processes with Jump points (CMJs) which can model continuous time, feedback loops, and non-stationary distributions. Second, I characterize constraint-based causal discovery under the CMJ framework using a data type which I call mixture data, or data created by sampling from a variety of unknown time points from the CMJ. The CMJ may for example correspond to a disease process, and the samples in a mixture dataset to cross-sectional data of patients at different stages in the disease. I finally propose a sound modification of FCI called the Fast Causal Inference with Feedback (F2CI) algorithm which uses conditional independence testing and conditional mixture modeling to infer causal structure from mixture data even when feedback loops, non-stationary distributions, selection bias and/or latent variables are present. Experiments suggest that the F2CI algorithm outperforms FCI by a large margin in correctly identifying causal relations when non-stationary distributions and/or feedback loops exist.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Strobl, Ericericvonstrobl@gmail.comevs17
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairVisweswaran, Shyamshv3@pitt.edushv3
Committee MemberCooper, Gregorygfc@pitt.edugfc
Committee MemberLandsittel, Douglasdpl12@pitt.edudpl12
Committee MemberZhang,
Committee MemberSpirtes,
Date: 20 July 2017
Date Type: Publication
Defense Date: 23 June 2017
Approval Date: 20 July 2017
Submission Date: 19 July 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 116
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: causal discovery, feedback loops, FCI
Related URLs:
Date Deposited: 20 Jul 2017 17:25
Last Modified: 20 Jul 2017 17:25


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