CaltechAUTHORS: Monograph
https://feeds.library.caltech.edu/people/Eberhardt-Frederick/monograph.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenFri, 06 Sep 2024 18:56:37 -0700Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
https://resolver.caltech.edu/CaltechAUTHORS:20180613-135346984
Year: 2018
DOI: 10.48550/arXiv.1804.02747
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when P(X∣Y,Z)=P(X∣Y), Z is not useful as a feature to predict X, as long as Y is also a regressor. On the contrary, if P(X∣Y,Z)≠P(X∣Y), Z might improve prediction results. FIT applies to thousand-dimensional random variables with a hundred thousand samples in a fraction of the time required by alternative methods. We provide an extensive evaluation that compares FIT to six extant nonparametric independence tests. The evaluation shows that FIT has low probability of making both Type I and Type II errors compared to other tests, especially as the number of available samples grows. Our implementation of FIT is publicly available.https://resolver.caltech.edu/CaltechAUTHORS:20180613-135346984Estimating Causal Direction and Confounding of Two Discrete Variables
https://resolver.caltech.edu/CaltechAUTHORS:20190327-085917121
Year: 2019
DOI: 10.48550/arXiv.1611.01504
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presupposes that the probability distributions P(C) of a cause C is independent from the probability distribution P(E∣C) of the cause-effect mechanism. While our classifier is trained with a Bayesian assumption of flat hyperpriors, we do not make this assumption about our test data. This work connects to recent developments on the identifiability of causal models over continuous variables under the assumption of "independent mechanisms". Carefully-commented Python notebooks that reproduce all our experiments are available online at http://vision.caltech.edu/~kchalupk/code.html.https://resolver.caltech.edu/CaltechAUTHORS:20190327-085917121ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions
https://resolver.caltech.edu/CaltechAUTHORS:20200527-101434154
Year: 2020
DOI: 10.48550/arXiv.1906.02385
In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may help to speed up methods based on Answer Set Programming. However, this line of work has so far only considered the discovery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.https://resolver.caltech.edu/CaltechAUTHORS:20200527-101434154Intracranial electrical stimulation alters meso-scale network integration as a function of network topology
https://resolver.caltech.edu/CaltechAUTHORS:20210831-204705170
Year: 2021
DOI: 10.1101/2021.01.16.426941
Human brain dynamics are organized into a multi-scale network structure that contains multiple tight-knit, meso-scale communities. Recent work has demonstrated that many psychological capacities, as well as impairments in cognitive function secondary to damage, can be mapped onto organizing principles at this mesoscopic scale. However, we still don't know the rules that govern the dynamic interactions between regions that are constrained by the topology of the broader network. In this preregistered study, we utilized a unique human dataset in which whole brain BOLD-fMRI activity was recorded simultaneously with intracranial electrical stimulation, to characterize the effects of direct neural stimulation on the dynamic reconfiguration of the broader network. Direct neural stimulation increased the extent to which the stimulation site's own mesoscale community integrated with the rest of the brain. Further, we found that these network changes depended on the topological role of the stimulation site itself: stimulating regions with high participation coefficients led to global integration, whereas stimulating sites with low participation coefficients integrated that regions' own community with the rest of the brain. These findings provide direct causal evidence for how network topology shapes and constrains inter-regional coordination, and suggest applications for targeted therapeutic interventions in patients with deep-brain stimulation.https://resolver.caltech.edu/CaltechAUTHORS:20210831-204705170