CaltechAUTHORS: Article
https://feeds.library.caltech.edu/people/Eberhardt-Frederick/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenFri, 06 Sep 2024 18:56:37 -0700Experiment Selection for Causal Discovery
https://resolver.caltech.edu/CaltechAUTHORS:20140123-114755984
Year: 2013
Randomized controlled experiments are often described as the most reliable tool available to scientists
for discovering causal relationships among quantities of interest. However, it is often unclear
how many and which different experiments are needed to identify the full (possibly cyclic) causal
structure among some given (possibly causally insufficient) set of variables. Recent results in the
causal discovery literature have explored various identifiability criteria that depend on the assumptions
one is able to make about the underlying causal process, but these criteria are not directly
constructive for selecting the optimal set of experiments. Fortunately, many of the needed constructions
already exist in the combinatorics literature, albeit under terminology which is unfamiliar to
most of the causal discovery community. In this paper we translate the theoretical results and apply
them to the concrete problem of experiment selection. For a variety of settings we give explicit
constructions of the optimal set of experiments and adapt some of the general combinatorics results
to answer questions relating to the problem of experiment selection.https://resolver.caltech.edu/CaltechAUTHORS:20140123-114755984Experimental Indistinguishability of Causal Structures
https://resolver.caltech.edu/CaltechAUTHORS:20140227-092549193
Year: 2013
DOI: 10.1086/673865
Using a variety of different results from the literature, I show how causal discovery with experiments is limited unless substantive assumptions about the underlying causal structure are made. These results undermine the view that experiments, such as randomized controlled trials, can independently provide a gold standard for causal discovery. Moreover, I present a concrete example in which causal underdetermination persists despite exhaustive experimentation and argue that such cases undermine the appeal of an interventionist account of causation as its dependence on other assumptions is not spelled out.https://resolver.caltech.edu/CaltechAUTHORS:20140227-092549193Direct Causes and the Trouble with Soft Interventions
https://resolver.caltech.edu/CaltechAUTHORS:20141023-135945304
Year: 2014
DOI: 10.1007/s10670-013-9552-2
An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. I provide a new example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. I consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing. Part of the fallout is a clearer account of the difficulties in characterizing so-called "soft" interventions.https://resolver.caltech.edu/CaltechAUTHORS:20141023-135945304Equidynamics and reliable reasoning about frequencies
https://resolver.caltech.edu/CaltechAUTHORS:20170616-075028154
Year: 2015
DOI: 10.1007/s11016-014-9971-y
Strevens' Tychomancy is an important book for philosophers and historians of science, and for scientists interested in the processes by which we reason about probabilities and frequencies, or interested in the evolution of our ability to do so. Strevens notes that we often have very good intuitions about probability and frequencies in physical processes, and asks how and why that is so. He describes several closely related reasoning strategies that would justify such intuitions. These "equidynamic" (xi) reasoning strategies1 can be used to infer, beginning from relatively minimal assumptions, conclusions about frequencies of outcomes of physical processes involving complex interactions. Strevens illustrates the strategies by applying them to examples such as Maxwell's derivation of an equilibrium probability distribution over states of a gas, physical games of chance and similar systems, and organisms interacting in evolving populations. Strevens argues that we routinely engage in these reasoning strategies, largely through unconscious processes that are in some sense innate.https://resolver.caltech.edu/CaltechAUTHORS:20170616-075028154Green and grue causal variables
https://resolver.caltech.edu/CaltechAUTHORS:20160602-123514680
Year: 2016
DOI: 10.1007/s11229-015-0832-z
The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes (2007) showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no "correct" set of causal variables. I extend Spirtes' result to the general framework of linear structural equation models and then explore to what extent the possibility to intervene or a preference for simpler causal systems may help in selecting among sets of causal variables.https://resolver.caltech.edu/CaltechAUTHORS:20160602-123514680Multi-Level Cause-Effect Systems
https://resolver.caltech.edu/CaltechAUTHORS:20190329-151702979
Year: 2016
DOI: 10.48550/arXiv.1512.07942
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et. al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms.https://resolver.caltech.edu/CaltechAUTHORS:20190329-151702979Causal Discovery from Subsampled Time Series Data by Constraint Optimization
https://resolver.caltech.edu/CaltechAUTHORS:20170221-090923083
Year: 2016
DOI: 10.48550/arXiv.arXiv.1602.07970
PMCID: PMC5305170
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. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.https://resolver.caltech.edu/CaltechAUTHORS:20170221-090923083Causal Feature Learning: An Overview
https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152248
Year: 2017
DOI: 10.1007/s41237-016-0008-2
Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal inference framework rooted in the language of causal graphical models (Pearl J, Reasoning and inference. Cambridge University Press, Cambridge, 2009; Spirtes et al., Causation, Prediction, and Search. Massachusetts Institute of Technology, Massachusetts, 2000), and computational mechanics (Shalizi, PhD thesis, University of Wisconsin at Madison, 2001). CFL is aimed at discovering high-level causal relations from low-level data, and at reducing the experimental effort to understand confounding among the high-level variables. We first review the scientific motivation for CFL, then present a detailed introduction to the framework, laying out the definitions and algorithmic steps. A simple example illustrates the techniques involved in the learning steps and provides visual intuition. Finally, we discuss the limitations of the current framework and list a number of open problems.https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152248A constraint optimization approach to causal discovery from subsampled time series data
https://resolver.caltech.edu/CaltechAUTHORS:20171109-074339105
Year: 2017
DOI: 10.1016/j.ijar.2017.07.009
We consider causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. We then apply the method to real-world data, investigate the robustness and scalability of our method, consider further approaches to reduce underdetermination in the output, and perform an extensive comparison between different solvers on this inference problem. Overall, these advances build towards a full understanding of non-parametric estimation of system timescale causal structures from subsampled time series data.https://resolver.caltech.edu/CaltechAUTHORS:20171109-074339105Approximate Causal Abstraction
https://resolver.caltech.edu/CaltechAUTHORS:20200527-100350364
Year: 2019
DOI: 10.48550/arXiv.1906.11583
Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on prior work of Rubinstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model only offers an approximation of the underlying system. We show how the resulting account handles the discrepancy that can arise between low- and high-level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to probabilistic causal models, indicating how and where uncertainty can enter into an approximate abstraction.https://resolver.caltech.edu/CaltechAUTHORS:20200527-100350364Causal Mapping of Emotion Networks in the Human Brain: Framework and Initial Findings
https://resolver.caltech.edu/CaltechAUTHORS:20171115-073336929
Year: 2020
DOI: 10.1016/j.neuropsychologia.2017.11.015
PMCID: PMC5949245
Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI.https://resolver.caltech.edu/CaltechAUTHORS:20171115-073336929Personality beyond taxonomy
https://resolver.caltech.edu/CaltechAUTHORS:20201109-135419046
Year: 2020
DOI: 10.1038/s41562-020-00989-3
Human and animal behaviour exhibits complex but regular patterns over time, often referred to as expressions of personality. Yet it remains unclear what personality really is: is it just the behavioural patterns themselves, something in the brain, in the genes or perhaps all of these? Here we offer a set of causal hypotheses about the role of personality, integrating psychological and neuroscientific approaches to personality in a testable framework. These hypotheses clarify the causal and constitutive relations that personality has with genes, environment, brain, mind and behaviour, and we suggest specific experiments that can adjudicate amongst the different hypotheses. We focus on a set of models that propose that personality is instantiated in the brain, distally caused by genes and environment and, in turn, causing the overt behaviours from which it is often inferred. We argue that articulating and testing such models will be essential in a mature science of personality.https://resolver.caltech.edu/CaltechAUTHORS:20201109-135419046A contemporary example of Reichenbachian coordination
https://resolver.caltech.edu/CaltechAUTHORS:20220315-626013000
Year: 2022
DOI: 10.1007/s11229-022-03571-8
This article is an attempt to provide an example that illustrates Hans Reichenbach's concept of coordination. Throughout Reichenbach's career the concept of coordination played an important role in his understanding of the connection between reality and how it is scientifically described. Reichenbach never fully specified what coordination is and how exactly it works. Instead, we are left with a variety of hints and gestures, many not entirely consistent with each other and several that are subject to change over the course of his career. Using the example of how to discover and construct causal variables, I will show that most of the features of coordination that Reichenbach described can be instantiated together and formulated precisely.https://resolver.caltech.edu/CaltechAUTHORS:20220315-626013000Causal Emergence: When Distortions in a Map Obscure the Territory
https://resolver.caltech.edu/CaltechAUTHORS:20220309-981595000
Year: 2022
DOI: 10.3390/philosophies7020030
We provide a critical assessment of the account of causal emergence presented in Erik Hoel's 2017 article "When the map is better than the territory". The account integrates causal and information theoretic concepts to explain under what circumstances there can be causal descriptions of a system at multiple scales of analysis. We show that the causal macro variables implied by this account result in interventions with significant ambiguity, and that the operations of marginalization and abstraction do not commute. Both of these are desiderata that, we argue, any account of multi-scale causal analysis should be sensitive to. The problems we highlight in Hoel's definition of causal emergence derive from the use of various averaging steps and the introduction of a maximum entropy distribution that is extraneous to the system under investigation.https://resolver.caltech.edu/CaltechAUTHORS:20220309-981595000