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Jiji Zhang [15]Jijia Zhang [1]
  1. Jiji Zhang, Adjacency-Faithfulness and Conservative Causal Inference.
    Most causal discovery algorithms in the literature exploit an assumption usually referred to as the Causal Faithfulness or Stability Condition. In this paper, we highlight two components of the condition used in constraint-based algorithms, which we call “Adjacency-Faithfulness” and “Orientation- Faithfulness.” We point out that assuming Adjacency-Faithfulness is true, it is possible to test the validity of Orientation- Faithfulness. Motivated by this observation, we explore the consequence of making only the Adjacency-Faithfulness assumption. We show that the familiar PC algorithm has (...)
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  2. Jiji Zhang, Strong Faithfulness and Uniform Consistency in Causal Inference.
    A fundamental question in causal inference is whether it is possible to reliably infer the manipulation effects from observational data. There are a variety of senses of asymptotic reliability in the statistical literature, among which the most commonly discussed frequentist notions are pointwise consistency and uniform consistency (see, e.g. Bickel, Doksum [2001]). Uniform consistency is in general preferred to pointwise consistency because the former allows us to control the worst case error bounds with a finite sample size. In the sense (...)
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  3. Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence for Directed Maximal Ancestral Graphs.
    The conditional independence relations present in a data set usually admit multiple causal explanations — typically represented by directed graphs — which are Markov equivalent in that they entail the same conditional independence relations among the observed variables. Markov equivalence between directed acyclic graphs (DAGs) has been characterized in various ways, each of which has been found useful for certain purposes. In particular, Chickering’s transformational characterization is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is (...)
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  4. Rafael De Clercq, Wai-Yin Lam & Jiji Zhang (2014). Is There a Problem with the Causal Criterion of Event Identity? American Philosophical Quarterly 51 (2):109-119.
    In this paper, we take another look at the reasons for which the causal criterion of event identity has been abandoned. We argue that the reasons are not strong. First of all, there is a criterion in the neighborhood of the causal criterion—the counterfactual criterion—that is not vulnerable to any of the putative counterexamples brought up in the literature. Secondly, neither the causal criterion nor the counterfactual criterion suffers from any form of vicious circularity. Nonetheless, we do not recommend adopting (...)
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  5. Jiji Zhang (2013). A Lewisian Logic of Causal Counterfactuals. Minds and Machines 23 (1):77-93.
    In the artificial intelligence literature a promising approach to counterfactual reasoning is to interpret counterfactual conditionals based on causal models. Different logics of such causal counterfactuals have been developed with respect to different classes of causal models. In this paper I characterize the class of causal models that are Lewisian in the sense that they validate the principles in Lewis’s well-known logic of counterfactuals. I then develop a system sound and complete with respect to this class. The resulting logic is (...)
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  6. Jiji Zhang (2013). Can the Incompatibilist Get Past the No Past Objection? Dialectica 67 (3):345-352.
    I refute Bailey's claim that his argument for incompatibilism is immune to Campbell's No Past Objection. In my refutation I stress a simple point, that nomological necessitation by future world states does not undermine one's freedom with respect to the present world state. My analysis reveals that the No Past Objection challenges van Inwagen's second consequence argument about as much as it does the others, and suggests that the (uncompromising) incompatibilist must pursue some of the options that Bailey regarded as (...)
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  7. Jiji Zhang, Wai Yin Lam & Rafael de Clercq, A Peculiarity in Pearl's Logic of Interventionist Counterfactuals.
    We examine a formal semantics for counterfactual conditionals due to Judea Pearl, which formalizes the interventionist interpretation of counterfactuals central to the interventionist accounts of causation and explanation. We show that a characteristic principle validated by Pearl’s semantics, known as the principle of reversibility, states a kind of irreversibility: counterfactual dependence (in David Lewis’s sense) between two distinct events is irreversible. Moreover, we show that Pearl’s semantics rules out only mutual counterfactual dependence, not cyclic dependence in general. This, we argue, (...)
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  8. Jiji Zhang, Wai-Yin Lam & Rafael De Clercq (2013). A Peculiarity in Pearl's Logic of Interventionist Counterfactuals. Journal of Philosophical Logic 42 (5):783-794.
    We examine a formal semantics for counterfactual conditionals due to Judea Pearl, which formalizes the interventionist interpretation of counterfactuals central to the interventionist accounts of causation and explanation. We show that a characteristic principle validated by Pearl’s semantics, known as the principle of reversibility, states a kind of irreversibility: counterfactual dependence (in David Lewis’s sense) between two distinct events is irreversible. Moreover, we show that Pearl’s semantics rules out only mutual counterfactual dependence, not cyclic dependence in general. This, we argue, (...)
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  9. Cristina Bicchieri & Jiji Zhang, An Embarrassment of Riches : Modeling Social Preferences in Ultimatum Games.
    Experimental results in Ultimatum, Trust and Social Dilemma games have been interpreted as showing that individuals are, by and large, not driven by selfish motives. But we do not need experiments to know that. In our view, what the experiments show is that the typical economic auxiliary hypothesis of non-tuism should not be generalized to other contexts. Indeed, we know that when the experimental situation is framed as a market interaction, participants will be more inclined to keep more money, share (...)
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  10. Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng & Jiji Zhang (2010). Actual Causation: A Stone Soup Essay. 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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  11. Aitao Lu, Bert Hodges, Jijia Zhang & John X. Zhang (2009). Contextual Effects on Number–Time Interaction. Cognition 113 (1):117-122.
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  12. Jiji Zhang (2008). Error Probabilities for Inference of Causal Directions. Synthese 163 (3):409 - 418.
    A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness assumptions, the procedures (...)
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  13. Jiji Zhang, On the Completeness of Orientation Rules for Causal Discovery in the Presence of Latent Confounders and Selection Bias.
    Causal discovery becomes especially challenging when the possibility of latent confounding and/or selection bias is not assumed away. For this task, ancestral graph models are particularly useful in that they can represent the presence of latent confounding and selection effect, without explicitly invoking unobserved variables. Based on the machinery of ancestral graphs, there is a provably sound causal discovery algorithm, known as the FCI algorithm, that allows the possibility of latent confounders and selection bias. However, the orientation rules used in (...)
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  14. Jiji Zhang & Peter Spirtes (2008). Detection of Unfaithfulness and Robust Causal Inference. Minds and Machines 18 (2):239-271.
    Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to (...)
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  15. Jiji Zhang & Peter Spirtes, A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
    JiJi Zhang and Peter Spirtes. A Characterization of Markov Equivalence Classes for Ancestral Graphical Models.
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  16. Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence Between DAGs with Latent Variables.
    JiJi Zhang and Peter Spirtes. A Transformational Characterization of Markov Equivalence between DAGs with Latent Variables.
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