41 Matching Annotations
  1. Feb 2024
    1. The behaviour of a unit of code should be as obvious as possible by looking only at that unit of code

      Locality is also a principle in physics.

  2. Oct 2023
    1. counteringopinions start to speak out against causal AI/ML (Bishop, 2021)

      Should we read this paper as well? Is there an updated paper or opinion piece from these researchers about why causal AI/ML isn't needed?

    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. 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. 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. Aug 2023
    1. You probably solved not because of using aws-sdk gem instead of aws-sdk-s3 but simply because you updated the SDK. I’m sure just updating the aws-sdk-s3 would solved too.
  4. Jul 2022
    1. Building probabilistic causal models has always been a challenge. The direction of causality is often difficult to establish and the process of constructing the causal graph with the probabilities behind requires the input of a variety of domain knowledge experts. Moreover, collecting inputs from experts can be costly and inefficient. But what if expert knowledge can be mined directly from the web from thousands of daily published news articles (wisdom-of-the-crowds) through NLP techniques and streamlined through a fast and automated process that can produce a causal model in a matter of seconds? We discuss an approach in our latest paper:https://lnkd.in/eSDYJ7D#pgm #datascience #bayesiannetworks #causalmodels #artificialintelligence #machinelearning #nlp #finance Pierre Haren Dr. Olav Laudy Allen Ginsberg Marcos Lopez de Prado Gautier Marti Charles-Albert Lehalle Paul Bilokon, PhD Saeed Amen Matthew Dixon Igor Halperin N Joshua Madan Daphne Koller Kevin Murphy Joseph Simonian, Ph.D. Dr. Ron Dembo Alexander Fleiss
    1. “Combining these causal linkswith predictive analytics providesvaluable insights and forecasts onmacroeconomic and microeconomictopics such as market demands andtrends for CFOs to understand howtheir new strategies and investmentscould be perceived by the market,” saysPierre Haren, Ph.D., the CEO and co-founder of Causality Link.
    1. In practice this means the platform will integrate the dat-apoints drawn up by Causality Link’s analysis, togetherwith any other alternative dataset the manager has pur-chased, and overlay it with the firms’ internal analyst emailsand notes.
    2. The platform Causality Link performs both of these tasksfor managers. It provides a “wisdom of crowds” point ofview of the evolution of almost any driver in the world, butit also gives clients a unique causal model that has beenextracted from the knowledge of documents they don’thave the time to read.
    3. Causality Link’s AI-powered research platform extractsthe “causal knowledge” contained within millions of docu-ments and other text-based sources to provide investorsand analysts with a unique perspective on companies,industries and macroeconomics.
    4. “Our research assistance tool worksas the ultimate brain sitting in the middle of a firm, readingeverything on the portfolio managers’ behalf,” says EricJensen, Co-Founder and CTO at Causality Link.


  5. Dec 2021
  6. 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

  7. Apr 2021
  8. Jan 2021
    1. While no serious climate scientist doubts the fact that human activities are causing climate change, this can’t be proved through experimentation on another Earth.

      In both cases, the answers should be clear when looking at the evidence and the mechanisms at play without an ideological bias

  9. Oct 2020
  10. Aug 2020
  11. Jul 2020
  12. Jun 2020
  13. May 2020
  14. Apr 2020
    1. Watts, D. J., Beck, E. D., Bienenstock, E. J., Bowers, J., Frank, A., Grubesic, A., Hofman, J., Rohrer, J. M., & Salganik, M. (2018). Explanation, prediction, and causality: Three sides of the same coin? [Preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/u6vz5

  15. Mar 2018
    1. Assess-ing causality is one of the most needed futuredevelopments in SciSci: Many descriptive studiesreveal strong associations between structure andoutcomes, but the extent to which a specific struc-ture“causes”an outcome remains unexplored.
  16. Mar 2017
    1. A specific component cause mayplay a role in one, two, or all three of thecausal mechanisms pictured

      Figure 1 here is particularly important.

    2. “sufficient cause,” whichmeans a complete causal mechanism, can bedefined as a set of minimal conditions andevents that inevitably produce disease; “mini-mal” implies that all of the conditions orevents are necessary to that occurrence

      Set of minimal conditions together define the sufficient cause or complete causal mechanism.

    3. an event, condition, or characteristicthat preceded the disease event and withoutwhich the disease event either would nothave occurred at all or would not have oc-curred until some later time

      The expression, "without which the disease or event would not have occurred", points out to another important concept here, the notion of counterfactual theory of causation.

    4. We can define a cause of a specific dis-ease event as an antecedent event, condition,or characteristic that was necessary for theoccurrence of the disease at the moment itoccurred

      Note the criteria:

      1. Cause as an event
      2. Cause as a condition
      3. Cause as characteristic
      4. Cause is antecedent
      5. Cause is necessary
      6. Cause is conditional We will see all of these conditions expanded
    5. we need a moregeneral conceptual model that can serve as acommon starting point in discussions ofcausal theories

      Note that we start with the example of the bulb and then expand to generalise this example to larger issues -- in our case, health.

    6. Theeffect usually occurs immediately after turn-ing on the switch, and as a result we slip intothe frame of thinking in which we identify theswitch as a unique cause

      Another important point -- often the last observable event tends to be considered the "cause" of an outcome. One must be careful to find out other possible causes of an outcome.

    7. When allother factors are in place, turning the switchwill cause the light to go on, but if one ormore of the other factors is lacking, the lightwill not go on

      This is a very important point in this paper. It points to the notions of multifactorial causality -- that is, an outcome will more often than not have more than one cause, and one cause is not usually sufficient to result in the effect.