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  1. added 2020-05-26
    A Review of Proposed Principles of Causal Non-Monotonic Reasoning. [REVIEW]Patrick Marchisella - 2020 - Australasian Journal of Logic 17 (3):14.
    Within Non-monotonic Reasoning, numerous principles of causal reasoning have been proposed. Many of these principles have been viewed as desirable in formalisms that reason with causality, and have been widely adopted throughout the literature. We provide a critique of these principles, evaluate their suitability for characterising and formulating causal non-monotonic reasoning, and find that most are unsuitable. Further, we discuss a new approach to causal non-monotonic reasoning motivated by how humans typically reason with causality.
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  2. added 2020-05-19
    A Process Model of Causal Reasoning.Zachary J. Davis & Bob Rehder - 2020 - Cognitive Science 44 (5).
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  3. added 2020-05-13
    The Traditional Theory of Perception Comes Back to Life.D. L. C. MacLachlan - 2018 - Proceedings of the XXIII World Congress of Philosophy 75:157-161.
    The causal representative theory of perception dominated theory of knowledge for hundreds of years after it was put on the map by Descartes and Locke. It is now almost extinct. How could this happen? The theory collapsed because it could not explain how we acquire knowledge of the external world, since it presupposes a causally organized system of external objects producing sensations in us. This presupposition, however, is generally recognized as true, so that the pattern of causal inference at the (...)
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  4. added 2020-04-15
    The Developmental Profile of Temporal Binding: From Childhood to Adulthood.Sara Lorimer, Teresa McCormack, Emma Blakey, David A. Lagnado, Christoph Hoerl, Emma Tecwyn & Marc J. Buehner - forthcoming - Quarterly Journal of Experimental Psychology.
    Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6-7-, 7-8-, and 9-10- years) and adults on a novel interval estimation task. In Experiment 1, participants made judgments about the time interval between i) their button press and a rocket launch, and ii) a non-causal (...)
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  5. added 2020-04-15
    Temporal Binding, Causation and Agency: Developing a New Theoretical Framework.Christoph Hoerl, Sara Lorimer, Teresa McCormack, David A. Lagnado, Emma Blakey, Emma C. Tecwyn & Marc J. Buehner - 2020 - Cognitive Science 44 (e12843):1-27.
    In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency”. However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause-effect sequence. (...)
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  6. added 2020-04-06
    Unifying Gaussian LWF and AMP Chain Graphs to Model Interference.Jose M. Peña - 2020 - Journal of Causal Inference 8 (1).
    An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also (...)
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  7. added 2020-04-03
    A Counterfactual Explanation for the Action Effect in Causal Judgment.Paul Henne, Laura Niemi, Ángel Pinillos, Felipe De Brigard & Joshua Knobe - 2019 - Cognition 190:157-164.
    People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse (...)
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  8. added 2020-03-30
    The Explanatory Indispensability of Memory Traces.Felipe De Brigard - forthcoming - The Harvard Review of Philosophy.
    During the first half of the 20th century, many philosophers of memory opposed the postulation of memory traces based on the claim that a satisfactory account of remembering need not include references to causal processes involved in recollection. However, in 1966, an influential paper by Martin and Deutscher showed that causal claims are indeed necessary for a proper account of remembering. This, however, did not settle the issue, as in 1977 Malcolm argued that even if one were to buy Martin (...)
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  9. added 2020-03-09
    From Effect to Cause: Deductive Reasoning.Ricardo Tavares da Silva - 2019 - Kairos 22 (1):109-131.
    According to the traditional view, the following incompatibility holds true: in reasoning, either there is warrant (certainty) or there is novelty. If there is warrant, there is not novelty: that would be the case of deductive reasoning. If there is novelty, there is not warrant: that would be the case of inductive reasoning. Causal reasoning would belong to the second group because there is novelty and, therefore, there is not warrant in it. I argue that this is false: reasoning may (...)
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  10. added 2020-02-17
    Erratum To: Metaphysics, Prescription and Methodological Disagreement: A Comment on Mathias Frisch’s Causal Reasoning in Physics.Alexander Reutlinger, Phyllis Illari, Andreas Hüttemann & Mathias Frisch - 2016 - Metascience 25 (2):339-339.
  11. added 2020-02-17
    Metaphysics, Prescription and Methodological Disagreement: A Comment on Mathias Frisch’s Causal Reasoning in Physics.Alexander Reutlinger - 2015 - Metascience 24 (3):351-372.
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  12. added 2020-02-17
    An Explanatory Coherence Model of Decision Making in Ill-Structured Problems.M. Laura Frigotto & Alessandro Rossi - 2015 - Mind and Society 14 (1):35-55.
    Classical models of decision making deal fairly well with uncertainty, where settings are well-structured in terms of goals, alternatives, and consequences. Conversely, the typical ill-structured nature of strategy choices remains a challenge for extant models. Such cases can hardly build on the past, and their novelty makes the prediction of consequences a very difficult and poorly robust task. The weakness of the classical expected utility model in representing such problems has not been adequately solved by recent extensions. In this paper (...)
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  13. added 2020-02-17
    Hunting Side Effects and Explaining Them: Should We Reverse Evidence Hierarchies Upside Down?Barbara Osimani - 2014 - Topoi 33 (2):295-312.
    Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special attention to the privileged role assigned to randomized controlled trials in evidence based medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended effects, (...)
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  14. added 2020-02-17
    Epidemiological Causality.Alfredo Morabia - 2005 - History and Philosophy of the Life Sciences 27 (3/4):365 - 379.
    Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining (...)
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  15. added 2020-02-10
    Causation in Evidence-Based Medicine: In Reply to Strand and Parkkinen.Roger Kerry, Thor E. Eriksen, Svein A. Noer Lie, Stephen Mumford & Rani L. Anjum - 2014 - Journal of Evaluation in Clinical Practice 20 (6):985-987.
    Strand and Parkkinen criticize our dispositional account of causation in evidence‐based medicine for failing to provide a proper epistemology of causal knowledge. In particular, they claim that we do not explain how causal inferences should be drawn. In response, we point out that dispositionalism does indeed have an account of the epistemology of causation, including counterfactual dependence, intervention, prediction and clinical decision. Furthermore, we argue that this is an epistemology that fits better with the known fallibility of even our best‐informed (...)
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  16. added 2020-02-10
    Concepts of Disease and Diagnosis.Dana D. Copeland - 1977 - Perspectives in Biology and Medicine 20 (4):528-538.
    Medicine is the science and art of preventing, alleviating, or curing disease. Through diagnosis the physician identifies and names disease. While the concepts of disease and diagnosis are pivotal for medical science, philosophical ambiguities in both concepts persist and have great import for contemporary nosology. Unified concepts of disease have been built either on emphasis of exogenous causes of disease —xenochthonous models, or on emphasis of endogenous causes of diseases—autochthonous models. The choice between the two models is more than academic, (...)
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  17. added 2020-02-10
    Sir Ernest Laurence Kennaway FRS, 1881-1958: Chemical Causation of Cancer Then and Today.Alexander Haddow - 1974 - Perspectives in Biology and Medicine 17 (4):543-591.
  18. added 2020-01-21
    A Deliberative Approach to Causation.Fernandes Alison Sutton - 2017 - Philosophy and Phenomenological Research 95 (3):686-708.
    Fundamental physics makes no clear use of causal notions; it uses laws that operate in relevant respects in both temporal directions and that relate whole systems across times. But by relating causation to evidence, we can explain how causation fits in to a physical picture of the world and explain its temporal asymmetry. This paper takes up a deliberative approach to causation, according to which causal relations correspond to the evidential relations we need when we decide on one thing in (...)
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  19. added 2020-01-14
    Taking Control : The Role of Manipulation in Theories of Causation.Henning Strandin - 2019 - Dissertation, Stockholm University
    Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected interventionist theory (...)
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  20. added 2020-01-13
    Norms Affect Prospective Causal Judgments.Paul Henne, Kevin O'Neill, Paul Bello, Sangeet Khemlani & Felipe De Brigard - manuscript
    People more frequently select norm-violating factors, relative to norm- conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using only retrospective vignette-based paradigms. In within-participants designs, we use a novel set of videos to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Three experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We discuss these results (...)
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  21. added 2020-01-13
    Subjective causal networks and indeterminate suppositional credences.Jiji Zhang, Teddy Seidenfeld & Hailin Liu - forthcoming - Synthese:1-27.
    This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an agent, and interventional probabilities in (...)
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  22. added 2020-01-13
    A Psychological Approach to Causal Understanding and the Temporal Asymmetry.Elena Popa - forthcoming - Review of Philosophy and Psychology:1-18.
    This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not suffice for correct inferences of causal structure, (...)
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  23. added 2019-12-12
    Evidence and Explanation in Cicero's On Divination.Frank Cabrera - forthcoming - Studies in History and Philosophy of Science Part A.
    In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...)
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  24. added 2019-12-12
    Causality Influences Children's and Adults' Experience of Temporal Order.Emma C. Tecwyn, Christos Bechlivanidis, David A. Lagnado, Christoph Hoerl, Sara Lorimer, Emma Blakey, Teresa McCormack & Marc J. Buehner - 2020 - Developmental Psychology 56 (4):739-755.
    Although it has long been known that time is a cue to causation, recent work with adults has demonstrated that causality can also influence the experience of time. In causal reordering (Bechlivanidis & Lagnado, 2013, 2016) adults tend to report the causally consistent order of events, rather than the correct temporal order. However, the effect has yet to be demonstrated in children. Across four pre-registered experiments, 4- to 10-year-old children (N=813) and adults (N=178) watched a 3-object Michotte-style ‘pseudocollision’. While in (...)
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  25. added 2019-12-02
    Beyond Metaphors and Semantics: A Framework for Causal Inference in Neuroscience.Roberto A. Gulli - 2019 - Behavioral and Brain Sciences 42.
    The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.
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  26. added 2019-11-28
    Evidence-Based Medicine and Evaluativism.Tim Thornton - 2008 - Philosophy, Psychiatry, and Psychology 15 (2):175-178.
  27. added 2019-11-11
    Reply to Freedman.Richard Scheines - unknown
    In Causation, Prediction, and Search, we undertook a three part project. First, we characterized when causal models are indistinguishable by population conditional independence relations under several different assumptions relating causality to probability. Second, we proposed a number of algorithms that take sample data and optional background knowledge as input, and output a class of causal models compatible with the data and the background knowledge; the algorithms were accompanied by proofs of their correctness given assumptions that were clearly stated in CPS, (...)
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  28. added 2019-11-11
    Response to Franklin-Hall and Weslake on Stability and Proportionality.James Woodward - unknown
    This paper responds to criticisms of my account of the role of proportionality and stability in causal reasoning. It reformulates the notion of proportionality in response to objections.
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  29. added 2019-11-11
    Unboxing the Concepts in Newcomb’s Paradox: Causation, Prediction, Decision in Causal Knowledge Patterns.Roland Poellinger - manuscript
    In Nozick’s rendition of the decision situation given in Newcomb’s Paradox dominance and the principle of maximum expected utility recommend different strategies. While evidential decision theory seems to be split over which principle to apply and how to interpret the principles in the first place, causal decision theory seems to go for the solution recommended by dominance. As a reply to the CDT proposal by Wolfgang Spohn, who opts for “one-boxing” by employing reflexive decision graphs, I will draw on the (...)
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  30. added 2019-11-11
    Evidence Amalgamation in the Sciences: An Introduction.Roland Poellinger, Jürgen Landes & Samuel Fletcher - 2019 - Synthese 196 (8):3163-3188.
    Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions (...)
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  31. added 2019-11-11
    The Evaluation of Discovery: Models, Simulation and Search Through “Big Data”.Kun Zhang, Joseph D. Ramsey & Clark Glymour - 2019 - Open Philosophy 2 (1):39-48.
    A central theme in western philosophy was to find formal methods that can reliably discover empirical relationships and their explanations from data assembled from experience. As a philosophical project, that ambition was abandoned in the 20th century and generally dismissed as impossible. It was replaced in philosophy by neo-Kantian efforts at reconstruction and justification, and in professional statistics by the more limited ambition to estimate a small number of parameters in pre-specified hypotheses. The influx of “big data” from climate science, (...)
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  32. added 2019-11-11
    Technical Considerations in the Use of the E-Value.Tyler J. VanderWeele, Peng Ding & Maya Mathur - 2019 - Journal of Causal Inference 7 (2).
    The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have elsewhere proposed that the reporting of E-values for estimates and for the limit of the confidence interval closest to the null become routine whenever causal effects are of interest. A number of questions have arisen about the (...)
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  33. added 2019-11-11
    Case Selection and Causal Inferences in Qualitative Comparative Research.Thomas Plümper, Vera Troeger & Eric Neumayer - 2019 - PLoS ONE 14 (7).
    Traditionally, social scientists perceived causality as regularity. As a consequence, qualitative comparative case study research was regarded as unsuitable for drawing causal inferences since a few cases cannot establish regularity. The dominant perception of causality has changed, however. Nowadays, social scientists define and identify causality through the counterfactual effect of a treatment. This brings causal inference in qualitative comparative research back on the agenda since comparative case studies can identify counterfactual treatment effects. We argue that the validity of causal inferences (...)
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  34. added 2019-11-11
    On the Interpretation of D o ( X )Do.Judea Pearl - 2019 - Journal of Causal Inference 7 (1).
    This paper provides empirical interpretation of the d o do operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view d o do as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; (...)
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  35. added 2019-11-11
    New Traffic Conflict Measure Based on a Potential Outcome Model.Kentaro Yamada & Manabu Kuroki - 2019 - Journal of Causal Inference 7 (1).
    A key issue in the analysis of traffic accidents is to quantify the effectiveness of a given evasive action taken by a driver to avoid crashing. Since 1977, the widely accepted definition for this effectiveness measure, which is called traffic conflict, has been “the risk of a collision if the driver movement remains unchanged.” Although the definition is expressed counterfactually, the full power of counterfactual analysis was not utilized. In this paper, we propose a counterfactual measure of traffic conflict called (...)
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  36. added 2019-11-11
    Disentangling Mechanisms From Causes: And the Effects on Science.John Protzko - 2018 - Foundations of Science 23 (1):37-50.
    Despite the miraculous progress of science—it’s practitioners continue to run into mistakes, either discrediting research unduly or making leaps of causal inference where none are warranted. In this we isolate two of the reasons for such behavior involving the misplaced understanding of the role of mechanisms and mechanistic knowledge in the establishment of cause-effect relationships. We differentiate causal knowledge into causes, effects, mechanisms, cause-effect relationships, and causal stories. Failing to understand the role of mechanisms in this picture, including their absence (...)
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  37. added 2019-11-11
    What is Gained From Past Learning.Judea Pearl - 2018 - Journal of Causal Inference 6 (1).
    We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.
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  38. added 2019-11-11
    Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes.Zhiwei Zhang, Chunling Liu, Shujie Ma & Min Zhang - 2018 - Journal of Causal Inference 7 (1).
    Mann–Whitney-type causal effects are clinically relevant, easy to interpret, and readily applicable to a wide range of study settings. This article considers estimation of such effects when the outcome variable is a survival time subject to right censoring. We derive and discuss several methods: an outcome regression method based on a regression model for the survival outcome, an inverse probability weighting method based on models for treatment assignment and censoring, and two doubly robust methods that involve both types of models (...)
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  39. added 2019-11-11
    Investigating Causal Effects of Mental Events in Cognitive Neuroscience.Mikkel C. Vinding - unknown
    Mental causation is a predominantly theoretical topic rather than a topic studied in the laboratory. The purpose of this paper is to outline a general approach for studying mental causation by empirical means for philosophers and scientists interested in the topic. The aim is to outline how we can infer mental causation by empirical methods given an unknown solution to the mind-body problem. The approach is based on the principles of causal inference to find causal relations among observed variables used (...)
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  40. added 2019-11-11
    Stable Causal Relationships Are Better Causal Relationships.Nadya Vasilyeva, Thomas Blanchard & Tania Lombrozo - 2018 - Cognitive Science 42 (4):1265-1296.
    We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization’s actual scope of (...)
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  41. added 2019-11-11
    Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population.Sofrygin Oleg & J. van der Laan Mark - 2017 - Journal of Causal Inference 5 (1).
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  42. added 2019-11-11
    Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions.Judea Pearl - 2017 - Journal of Causal Inference 5 (2).
    The structural interpretation of counterfactuals as formulated in Balke and Pearl [.
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  43. added 2019-11-11
    Entropy Balancing is Doubly Robust.Zhao Qingyuan & Percival Daniel - 2017 - Journal of Causal Inference 5 (1).
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  44. added 2019-11-11
    Counterfactual-Based Prevented and Preventable Proportions.Kentaro Yamada & Manabu Kuroki - 2017 - Journal of Causal Inference 5 (2).
    Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range.
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  45. added 2019-11-11
    Determinantal Generalizations of Instrumental Variables.Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, Kaie Kubjas Reginald Mcgee Ii, McGee I. I. Reginald, Nhan Nguyen, Elina Robeva & Mathias Drton - 2017 - Journal of Causal Inference 6 (1).
    Linear structural equation models relate the components of a random vector using linear interdependencies and Gaussian noise. Each such model can be naturally associated with a mixed graph whose vertices correspond to the components of the random vector. The graph contains directed edges that represent the linear relationships between components, and bidirected edges that encode unobserved confounding. We study the problem of generic identifiability, that is, whether a generic choice of linear and confounding effects can be uniquely recovered from the (...)
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  46. added 2019-11-11
    Longitudinal Mediation Analysis with Time-Varying Mediators and Exposures, with Application to Survival Outcomes.Wenjing Zheng & Mark van der Laan - 2017 - Journal of Causal Inference 5 (2).
    :1 In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes. At a given time point, the exposure and mediator of interest are influenced by past covariates, mediators and exposures, and affect future covariates, mediators and exposures. Right censoring, if present, occurs in response to past history. To address the challenges in mediation analysis that are unique to these settings, we propose a formulation in terms (...)
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  47. added 2019-11-11
    Generalized Structural Mean Models for Evaluating Depression as a Post-Treatment Effect Modifier of a Jobs Training Intervention.Alisa Stephens, Luke Keele & Marshall Joffe - 2016 - Journal of Causal Inference 4 (2).
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  48. added 2019-11-11
    Lord’s Paradox Revisited –.Judea Pearl - 2016 - Journal of Causal Inference 4 (2).
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  49. added 2019-11-11
    The Sure-Thing Principle.Judea Pearl - 2016 - Journal of Causal Inference.
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  50. added 2019-11-11
    The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases.Peter M. Steiner & Yongnam Kim - 2016 - Journal of Causal Inference 4 (2).
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