27 found
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Jiji Zhang [26]Jijia Zhang [1]
  1.  78
    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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  2.  41
    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 (...)
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  3.  3
    Peter Spirtes & Jiji Zhang, A Uniformly Consistent Estimator of Causal Effects Under the K-Triangle-Faithfulness Assumption.
    Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and B¨uhlmann (...)
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  4.  54
    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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  5.  7
    Jiji Zhang & Peter Spirtes (forthcoming). The Three Faces of Faithfulness. Synthese:1-17.
    In the causal inference framework of Spirtes, Glymour, and Scheines , inferences about causal relationships are made from samples from probability distributions and a number of assumptions relating causal relations to probability distributions. The most controversial of these assumptions is the Causal Faithfulness Assumption, which roughly states that if a conditional independence statement is true of a probability distribution generated by a causal structure, it is entailed by the causal structure and not just for particular parameter values. In this paper (...)
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  6.  21
    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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  7.  77
    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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  8.  9
    Jiji Zhang & Kun Zhang (2015). Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence. Philosophy of Science 82 (5):930-940.
    Forster presented some interesting examples having to do with distinguishing the direction of causal influence between two variables, which he argued are counterexamples to the likelihood theory of evidence. In this article, we refute Forster’s arguments by carefully examining one of the alleged counterexamples. We argue that the example is not convincing as it relies on dubious intuitions that likelihoodists have forcefully criticized. More important, we show that contrary to Forster’s contention, the consilience-based methodology he favored is accountable within the (...)
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  9.  2
    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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  10.  10
    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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  11.  3
    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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  12.  41
    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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  13.  1
    Kun Zhang, Zhikun Wang, Jiji Zhang & Bernhard Scholkopf, On Estimation of Functional Causal Models : General Results and Application to the Post-Nonlinear Causal Model.
    Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model, nonlinear additive noise model, and post-nonlinear model. Currently, there are two ways to estimate the parameters in the models: dependence (...)
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  14.  26
    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 (...)
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  15.  12
    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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  16.  6
    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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  17.  17
    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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  18.  13
    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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  19.  8
    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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  20.  8
    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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  21. Ayesha Ali, Thomas Richardson, Peter Spirtes & Jiji Zhang, Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables.
    It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional (...)
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  22. Ricardo Silva, Jiji Zhang & James G. Shanshan, Probabilistic Workflow Mining.
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  23. Jiji Zhang, A Characterization of Markov Qquivalence Classes for Directed Acyclic Graphs with Latent Variables.
    Different directed acyclic graphs may be Markov equivalent in the sense that they entail the same conditional indepen- dence relations among the observed variables. Meek characterizes Markov equiva- lence classes for DAGs by presenting a set of orientation rules that can correctly identify all arrow orienta- tions shared by all DAGs in a Markov equiv- alence class, given a member of that class. For DAG models with latent variables, maxi- mal ancestral graphs provide a neat representation that facilitates model search. (...)
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  24. Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables.
    Different directed acyclic graphs may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Chickering provided a transformational characterization of Markov equivalence for DAGs, which is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is needed to prove the asymptotic correctness of a search procedure over Markov equivalence classes, known as the GES algorithm. For DAG models with latent variables, maximal ancestral graphs provide a neat representation (...)
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  25. Jiji Zhang, Causal Reasoning with Ancestral Graphical Models.
    Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate post-intervention probabilities to pre-intervention probabilities that are estimable from observational data. However, such causal diagrams are (...)
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  26. Jiji Zhang, Generalized Do-Calculus with Testable Causal Assumptions.
    A primary object of causal reasoning concerns what would happen to a system under certain interventions. Specifically, we are often interested in estimating the probability distribution of some random variables that would result from forcing some other variables to take certain values. The renowned do-calculus gives a set of rules that govern the identification of such post-intervention probabilities in terms of pre-intervention probabilities, assuming available a directed acyclic graph that represents the underlying causal structure. However, a DAG causal structure is (...)
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  27. Jiji Zhang, Underdetermination in Causal Inference.
    One conception of underdetermination is that it corresponds to the impossibility of reliable inquiry. In other words, underdetermination is defined to be the situation where, given a set of background assumptions and a space of hypotheses, it is logically impossible for any hypothesis selection method to meet a given reliability standard. From this perspective, underdetermination in a given subject of inquiry is a matter of interplay between background assumptions and reliability or success criteria. In this paper I discuss underdetermination in (...)
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