60 Matching Annotations
  1. Nov 2023
    1. In a partially ordered system it is still possible to enforce a to-tal order on events after the fact, as illustrated in Figure 2. Wedo this by attaching a logical timestamp to each event; Lamporttimestamps [45] are a common choice.
    2. However, other eventsmay be concurrent, which means that neither happened before theother; in this case, different replicas may process those events in adifferent order [10].
  2. Oct 2023
    1. Kallus, N. (2020). DeepMatch: Balancing deep covariate representations for causal inference using adversarial training. In I. H. Daumé, & A. Singh (Eds.), Proceedings of the 37th international conference on machine learning. In Proceedings of Machine Learning Research: vol. 119 (pp. 5067–5077). PMLR

    2. Using adversarial deep learning approaches to get a better correction for causal inference from observational data.

    1. "Causal Deep Learning" Authors:Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

      Very general and ambitious approach for representing the full continuous conceptual spectrum of Pearl's Causal Ladder, and ability to model and learning parts of this from Data.

      Discussed by Prof. van der Shaar at ICML2023 workshop on Counterfactuals.

    1. Performing optimization in the latent space can more flexibly model underlying data distributions than mechanistic approaches in the original hypothesis space. However, extrapolative prediction in sparsely explored regions of the hypothesis space can be poor. In many scientific disciplines, hypothesis spaces can be vastly larger than what can be examined through experimentation. For instance, it is estimated that there are approximately 1060 molecules, whereas even the largest chemical libraries contain fewer than 1010 molecules12,159. Therefore, there is a pressing need for methods to efficiently search through and identify high-quality candidate solutions in these largely unexplored regions.

      Question: how does this notion of hypothesis space relate to causal inference and reasoning?

    1. Causal Deep Learning Authors:Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

      Very general and ambitious approach for representing the full continuous conceptual spectrum of Pearl's Causal Ladder, and ability to model and learning parts of this from Data.

      Discussed by Prof. van der Shaar at ICML2023 workshop on Counterfactuals.

    1. (Cousineau,Verter, Murphy and Pineau, 2023) " Estimating causal effects with optimization-based methods: A review and empirical comparison"

    1. To avoid such bias, a fundamental aspect in the research design of studies of causalinference is the identification strategy: a clear definition of the sources of variation in the datathat can be used to estimate the causal effect of interest.

      To avoid making false conclusions, studies must identify all the sources of variation. Is this is even possible in most caes?

    2. Matching: This approach seeks to replicate a balanced experimental design usingobservational data by finding close matches between pairs or groups of units andseparating out the ones that received a specified treatment from those that did not, thusdefining the control groups.

      Matching approach to dealing with sampling bias. Basically use some intrinsic, or other, metric about the situations to cluster them so that "similar" situations will be dealt with similiarly. Then analysis is carried out on those clusters. Number of clusters has to be defined, some method, like k-means, if often used. Depends a lot on the similarity metric, the clustering approach, other assumptions

    3. Terwiesch, 2022 - "A review of Empircal Operations Managment over the Last Two Decades" Listed as an important review of methods for addressing biases in Operations management by explicitly addressing causality.

    1. Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, and Mark Crowley Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting In International Conference on Machine Learning (ICML). Honolulu, Hawaii, USA. Jul, 2023.

    1. "Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning" Yuejiang Liu1, 2,* YUEJIANG.LIU@EPFL.CH Alexandre Alahi2 ALEXANDRE.ALAHI@EPFL.CH Chris Russell1 CMRUSS@AMAZON.DE Max Horn1 HORNMAX@AMAZON.DE Dominik Zietlow1 ZIETLD@AMAZON.DE Bernhard Sch ̈olkopf1, 3 BS@TUEBINGEN.MPG.DE Francesco Locatello1 LOCATELF@AMAZON.DE

  3. Sep 2023
    1. Recent work has revealed several new and significant aspects of the dynamics of theory change. First, statistical information, information about the probabilistic contingencies between events, plays a particularly important role in theory-formation both in science and in childhood. In the last fifteen years we’ve discovered the power of early statistical learning.

      The data of the past is congruent with the current psychological trends that face the education system of today. Developmentalists have charted how children construct and revise intuitive theories. In turn, a variety of theories have developed because of the greater use of statistical information that supports probabilistic contingencies that help to better inform us of causal models and their distinctive cognitive functions. These studies investigate the physical, psychological, and social domains. In the case of intuitive psychology, or "theory of mind," developmentalism has traced a progression from an early understanding of emotion and action to an understanding of intentions and simple aspects of perception, to an understanding of knowledge vs. ignorance, and finally to a representational and then an interpretive theory of mind.

      The mechanisms by which life evolved—from chemical beginnings to cognizing human beings—are central to understanding the psychological basis of learning. We are the product of an evolutionary process and it is the mechanisms inherent in this process that offer the most probable explanations to how we think and learn.

      Bada, & Olusegun, S. (2015). Constructivism Learning Theory : A Paradigm for Teaching and Learning.

  4. Jul 2023
  5. Mar 2023
    1. // Insight Maker is used to model system dynamics and create agent based models by creating causal loop diagrams and allowing users to run simulations on those

    1. The term "immortal time" refers to a period of time during which an individual is not at risk of the outcome of interest, either because they have not yet been exposed to the treatment or intervention, or because they have not yet reached a certain point in time when the outcome can occur. During this time, the individual is "immortal" in the sense that they cannot experience the outcome, even if they would have if they had been at risk.

      Definition of immortal time bias

  6. Aug 2022
  7. Jul 2022
    1. he distinguishes three dimensions of dependent origination and this is in his commentary on the guardian of malama jamaica carica called clear words he talks about causal dependence that is every phenomenon depends upon causes and 00:16:19 conditions and gives rise to further causes and conditions um myriological dependence that is every phenomenon every composite phenomenon depends upon the parts that uh that it 00:16:31 comprises and every phenomenon is also dependent upon the holes or the systems in which it figures parts depend on holes holes depend on parts and that reciprocal meteorological dependence 00:16:44 characterizes all of reality and third often overlooked but most important is dependence on conceptual imputation that is things depend in order to be represented as the kinds of 00:16:57 things they are on our conceptual resources our affective resources and as john dunn emphasized our purposes in life this third one really means this um 00:17:09 everything that shows up for us in the world the way we carve the world up the way we um the way we experience the world is dependent not just on how the world is but on the conceptual resources 00:17:22 as well as the perceptual resources through which we understand the world and it's worth recognizing that um when we think about this there are a bunch of um contemporary majamakers majamikas we 00:17:34 might point to as well and so paul fireauben who's up there on on the left well really an austrian but he spent much of his life in america um willard van norman kwine um up on the right wilford sellers and paul churchland

      This is a key statement: how we experience the world depends on the perceptual and cognitive lens used to filter the world through.

      Francis Heylighen proposes a nondual system based on causal dependency relationships to serve as the foundation for distributed cognition.(collective intelligence).


  8. May 2022
    1. The hyperthreat can be outmaneuvered by humans reconfiguring their activities in two ways: security by design and security by dispersal. National security in the Anthropocene is increasingly achieved by designing systems and settlements so that enhanced security is incorporated from the start. For example, it can be imagined that each time a person refuels a car with petrol, this action empowers the hyperthreat. This leads to global warming, which creates ocean acidification and in turn reduced fish stocks, while also creating pressures for resource wars, thereby influencing whether a soldier or civilian dies and how much taxpayer resources are required for material security missions. In contrast, zero-emission transportation technologies can “design out” the slow violence and threats associated with a fossil-fuel-intensive lifestyle. This is similar for plastic use, in which case the “threat” is embodied in the high polluting design of consumable products and lifestyle activities. Likewise, other health threats and longer-term costs are embodied in hidden toxins or sugars in food products. Accordingly, peace, health, and a different form of national prosperity can be created through design, which requires a longer-term and mesh-intervention viewpoint. OP VAK has a role to play in achieving security and safety by design by linking apparently benign activities with their devastating impacts.    

      Linking these many fragmented and long causal chains and tracing them back to the hyperthreat can be a polwerful visualization that brings the hyperthreat to life.

  9. Apr 2022
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  14. Sep 2021
    1. Haber, N. A., Wieten, S. E., Rohrer, J. M., Arah, O. A., Tennant, P. W. G., Stuart, E. A., Murray, E. J., Pilleron, S., Lam, S. T., Riederer, E., Howcutt, S. J., Simmons, A. E., Leyrat, C., Schoenegger, P., Booman, A., Dufour, M.-S. K., O’Donoghue, A. L., Baglini, R., Do, S., … Fox, M. P. (2021). Causal and Associational Linking Language From Observational Research and Health Evaluation Literature in Practice: A systematic language evaluation [Preprint]. Epidemiology. https://doi.org/10.1101/2021.08.25.21262631

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  22. Oct 2020
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  28. Mar 2019
    1. Thisaccount refuses the representationalist fixation on “words” and “things”and the problematic of their relationality, advocating insteadacausalrelationship between specific exclusionary practices embodied as specific ma-terial configurations of the world(i.e., discursive practices/(con)figurationsrather than “words”)and specific material phenomena(i.e., relations ratherthan “things”). This causal relationship between the apparatuses of bodilyproduction and the phenomena produced is one of “agential intra-action.”The details follow

      Intro to "Agential Intra-Action"

  29. Feb 2017
    1. fact

      So Campbell seems to have a lot of "causal chains," so where are the "bundles of evidence" exactly? I mean, this definitely seems to be moral reasoning, but this looks like more of a chain than a bundle.

  30. Jan 2015
    1. Sobel, D. M. & Kirkham, N.Z. (2012). The influence of social information on children’s statistical and causal inferences. In F.Xu (Ed.). Rational constructivism in cognitive development.
    2. Sobel, D. M., & Kirkham, N. Z. (2006). Blickets and babies: The development of causal reasoning in toddlers and infants. Developmental Psychology, 42, 1103-1115.
    3. Sobel, D. M., & Kirkham, N. Z. (2007). Bayes nets and Babies: Infants’ developing representations of causal knowledge. Developmental Science, 10, 298-306.