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Frederick Eberhardt
California Institute of Technology
  1. Actual Causation: A Stone Soup Essay.Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines - 2010 - Synthese 175 (2):169-192.
    We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...)
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  2. Interventions and Causal Inference.Frederick Eberhardt & Richard Scheines - 2007 - Philosophy of Science 74 (5):981-995.
    The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...)
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  3. Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW]Frederick Eberhardt & David Danks - 2011 - Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  4.  55
    Direct Causes and the Trouble with Soft Interventions.Frederick Eberhardt - 2014 - Erkenntnis 79 (4):1-23.
    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 (...)
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  5. Reliability Via Synthetic a Priori: Reichenbach’s Doctoral Thesis on Probability.Frederick Eberhardt - 2011 - Synthese 181 (1):125-136.
    Hans Reichenbach is well known for his limiting frequency view of probability, with his most thorough account given in The Theory of Probability in 1935/1949. Perhaps less known are Reichenbach's early views on probability and its epistemology. In his doctoral thesis from 1915, Reichenbach espouses a Kantian view of probability, where the convergence limit of an empirical frequency distribution is guaranteed to exist thanks to the synthetic a priori principle of lawful distribution. Reichenbach claims to have given a purely objective (...)
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  6.  59
    Green and Grue Causal Variables.Frederick Eberhardt - 2016 - Synthese 193 (4).
    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 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 (...)
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  7. Introduction to the Epistemology of Causation.Frederick Eberhardt - 2009 - Philosophy Compass 4 (6):913-925.
    This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...)
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  8.  47
    Experimental Indistinguishability of Causal Structures.Frederick Eberhardt - 2013 - Philosophy of Science 80 (5):684-696.
    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 (...)
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  9.  23
    Hans Reichenbach's Probability Logic.Frederick Eberhardt & Clark Glymour - 2004 - In Dov M. Gabbay, John Woods & Akihiro Kanamori (eds.), Handbook of the History of Logic. Elsevier. pp. 10--357.
  10.  39
    N − 1 Experiments Suffice to Determine the Causal Relations Among N Variables.Frederick Eberhardt, Clark Glymour & Richard Scheines - unknown
    By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N - 1 experiments suffice to determine the causal relations among N > 2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N > 4 when each experiment can simultaneously randomize more than one variable. This bound provides a type of ideal for the measure of (...)
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  11.  8
    Combining Experiments to Discover Linear Cyclic Models with Latent Variables.Richard Scheines, Frederick Eberhardt & Patrik O. Hoyer - unknown
    We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or `soft' interventions on one or multiple variables at a time. The algorithm is `online' in the sense that it combines the results from (...)
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  12.  4
    Causal Emergence: When Distortions in a Map Obscure the Territory.Frederick Eberhardt & Lin Lin Lee - 2022 - Philosophies 7 (2):30.
    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 (...)
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  13.  10
    Conceptual Problems in Statistics, Testing and Experimentation.David Danks & Frederick Eberhardt - unknown
  14. Explaining Norms and Norms Explained.David Danks & Frederick Eberhardt - 2009 - Behavioral and Brain Sciences 32 (1):86-87.
    Oaksford & Chater (O&C) aim to provide teleological explanations of behavior by giving an appropriate normative standard: Bayesian inference. We argue that there is no uncontroversial independent justification for the normativity of Bayesian inference, and that O&C fail to satisfy a necessary condition for teleological explanations: demonstration that the normative prescription played a causal role in the behavior's existence.
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  15.  16
    Direct Cause.Frederick Eberhardt - unknown
    An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. We provide an example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. We 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.
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  16.  74
    A Sufficient Condition for Pooling Data.Frederick Eberhardt - 2008 - Synthese 163 (3):433 - 442.
    We consider the problems arising from using sequences of experiments to discover the causal structure among a set of variables, none of whom are known ahead of time to be an “outcome”. In particular, we present various approaches to resolve conflicts in the experimental results arising from sampling variability in the experiments. We provide a sufficient condition that allows for pooling of data from experiments with different joint distributions over the variables. Satisfaction of the condition allows for an independence test (...)
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  17.  40
    Equidynamics and Reliable Reasoning About Frequencies: Michael Strevens: Tychomancy: Inferring Probability From Causal Structure. Cambridge, MA: Harvard University Press, 265pp, $39.95 HB.Marshall Abrams, Frederick Eberhardt & Michael Strevens - 2015 - Metascience 24 (2):173-188.
    A symposium on Michael Strevens' book "Tychomancy", concerning the psychological roots and historical significance of physical intuition about probability in physics, biology, and elsewhere.
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  18.  24
    SAT-Based Causal Discovery Under Weaker Assumptions. Zhalama, Jiji Zhang, Frederick Eberhardt & Wolfgang Mayer - 2017 - In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI). Association for Uncertainty in Artificial Intelligence (AUAI).
    Using the flexibility of recently developed methods for causal discovery based on Boolean satisfiability solvers, we encode a variety of assumptions that weaken the Faithfulness assumption. The encoding results in a number of SAT-based algorithms whose asymptotic correctness relies on weaker conditions than are standardly assumed. This implementation of a whole set of assumptions in the same platform enables us to systematically explore the effect of weakening the Faithfulness assumption on causal discovery. An important effect, suggested by simulation results, is (...)
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  19. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI). Zhalama, Jiji Zhang, Frederick Eberhardt & Wolfgang Mayer - 2017 - Association for Uncertainty in Artificial Intelligence (AUAI).
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  20.  24
    Keeping Bayesian Models Rational: The Need for an Account of Algorithmic Rationality.David Danks & Frederick Eberhardt - 2011 - Behavioral and Brain Sciences 34 (4):197-197.
    We argue that the authors’ call to integrate Bayesian models more strongly with algorithmic- and implementational-level models must go hand in hand with a call for a fully developed account of algorithmic rationality. Without such an account, the integration of levels would come at the expense of the explanatory benefit that rational models provide.
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  21.  1
    A contemporary example of Reichenbachian coordination.Frederick Eberhardt - 2022 - Synthese 200 (2):1-14.
    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 (...)
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