• for: social tipping point - tools, cascade institute, Boolean Causal Loop Analysis, BCLA, Cross-impact balance, CIB, socio-cognitive mapping, cognitive-affective mapping, ideological state-space

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

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

"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.

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

[ Bengio, The Consciousness Prior, Arxiv, 2018]

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.

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

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

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

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.

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.

"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

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.

// 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

Definition of immortal time bias

McGinley, L., & Sun, L. (n.d.). FDA adds new warning on Johnson & Johnson vaccine related to rare autoimmune disorder. Washington Post. Retrieved July 13, 2021, from https://www.washingtonpost.com/health/2021/07/12/johnson-and-johnson-warning/

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).

https://hyp.is/go?url=https%3A%2F%2Fbafybeicho2xrqouoq4cvqev3l2p44rapi6vtmngfdt42emek5lyygbp3sy.ipfs.dweb.link%2FNon-dualism%2520-%2520Mind%2520outside%2520Brain%2520%2520a%2520radically%2520non-dualist%2520foundation%2520for%2520distributed%2520cognition.pdf&group=world

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.

ReconfigBehSci [@SciBeh]. ‘RT @CAUSALab: Interested in #causalinference? Learn from Top Experts in the Field. Summer Courses Offered at the Harvard T.H. Chan Schoo…’. Tweet. Twitter, 20 December 2021. https://twitter.com/SciBeh/status/1483138177837715464.

Nast, C. (2022, January 15). Do the Omicron Numbers Mean What We Think They Mean? The New Yorker. https://www.newyorker.com/magazine/2022/01/24/do-the-omicron-numbers-mean-what-we-think-they-mean

Sloman, S. A. (2021). How Do We Believe? Topics in Cognitive Science, 0(2021), 1–14. https://doi.org/10.1111/tops.12580

Portfolio, B. and S. S. at N. (2021, November 3). No evidence school closures reduce the spread of COVID-19. Behavioural and Social Sciences at Nature Portfolio. http://socialsciences.nature.com/posts/no-evidence-school-closures-reduce-the-spread-of-covid-19

Tran, V.-T., Perrodeau, E., Saldanha, J., Pane, I., & Ravaud, P. (2021). Efficacy of COVID-19 Vaccination on the Symptoms of Patients With Long COVID: A Target Trial Emulation Using Data From the ComPaRe e-Cohort in France (SSRN Scholarly Paper ID 3932953). Social Science Research Network. https://papers.ssrn.com/abstract=3932953

Maarten van Smeden on Twitter. (n.d.). Twitter. Retrieved 6 September 2021, from https://twitter.com/MaartenvSmeden/status/1432378998470889472

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

Are 7 French fries too many?. A causal inference explainer | by Ellie Murray | Medium. (n.d.). Retrieved August 22, 2021, from https://medium.com/@EpiEllie/are-7-french-fries-too-many-d6226e78dc1f

Snopes.com. “No, There Weren’t More COVID-19 Vaccine Deaths Than COVID-19 Deaths.” Accessed July 23, 2021. https://www.snopes.com/fact-check/covid-vax-deaths-vs-covid-deaths/.

Gugerty, L., Shreeves, M., & Dumessa, N. (2021). Biased belief updating in causal reasoning about COVID-19. PsyArXiv. https://doi.org/10.31234/osf.io/bfw76

Williamson, H. C. (2020). Early Effects of the COVID-19 Pandemic on Relationship Satisfaction and Attributions. Psychological Science, 31(12), 1479–1487. https://doi.org/10.1177/0956797620972688

Anthony Costello on Twitter. (n.d.). Twitter. Retrieved 1 March 2021, from https://twitter.com/globalhlthtwit/status/1323747414923202562

Rohrer, J. M., Schmukle, S., & McElreath, R. (2021). The Only Thing That Can Stop Bad Causal Inference Is Good Causal Inference. PsyArXiv. https://doi.org/10.31234/osf.io/mz5jx

Conley, D., & Johnson, T. (2021). Opinion: Past is future for the era of COVID-19 research in the social sciences. Proceedings of the National Academy of Sciences, 118(13). https://doi.org/10.1073/pnas.2104155118

Hernán, M. A., Clayton, D., & Keiding, N. (2011). The Simpson’s paradox unraveled. International Journal of Epidemiology, 40(3), 780–785. https://doi.org/10.1093/ije/dyr041

Quillien, Tadeg, and Michael Barlev. ‘Causal Judgment in the Wild: Evidence from the 2020 US Presidential Election’. PsyArXiv, 7 April 2021. https://doi.org/10.31234/osf.io/7w9re.

the Guardian. ‘There’s No Proof the Oxford Vaccine Causes Blood Clots. So Why Are People Worried? | David Spiegelhalter’, 15 March 2021. http://www.theguardian.com/commentisfree/2021/mar/15/evidence-oxford-vaccine-blood-clots-data-causal-links.

Levine, E. (2021). Community standards of deception: Deception is perceived to be ethical when it prevents unnecessary harm. PsyArXiv. https://doi.org/10.31234/osf.io/g5trb

Blakely, Tony, John Lynch, Koen Simons, Rebecca Bentley, and Sherri Rose. ‘Reflection on Modern Methods: When Worlds Collide—Prediction, Machine Learning and Causal Inference’. International Journal of Epidemiology 49, no. 6 (1 December 2020): 2058–64. https://doi.org/10.1093/ije/dyz132.

Peyton, Kyle, Gregory A. Huber, and Alexander Coppock. “The Generalizability of Online Experiments Conducted During The COVID-19 Pandemic.” SocArXiv, November 28, 2020. https://doi.org/10.31235/osf.io/s45yg.

Burel, Gregoire; Farrell, Tracie; Mensio, Martino; Khare, Prashant and Alani, Harith (2020). Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic. In: Proceedings of the 12th International Social Informatics Conference (SocInfo), LNCS.

Adams, R. C., Sumner, P., Vivian-Griffiths, S., Barrington, A., Williams, A., Boivin, J., Chambers, C. D., & Bott, L. (2017). How readers understand causal and correlational expressions used in news headlines. Journal of Experimental Psychology: Applied, 23(1), 1–14. https://doi.org/10.1037/xap0000100

Karl Friston: Up to 80% not even susceptible to Covid-19. (2020, June 4). UnHerd. https://unherd.com/2020/06/karl-friston-up-to-80-not-even-susceptible-to-covid-19/

The Alan Turing Institute: Causal Inference, Causal Decision Making Under Uncertainty | CogX 2020. (2020, June 25). https://www.youtube.com/watch?v=JAGRHbDLvUs

Gruijters, Stefan L.K. ‘The Fallacy of Manipulation “Checks” in Psychological Experiments’. Preprint. PsyArXiv, 20 August 2020. https://doi.org/10.31234/osf.io/fkzv5.

Leininger, A., & Schaub, M. (2020). Voting at the dawn of a global pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a32r7

Causal inference isn’t what you think it is. (n.d.). LSHTM. Retrieved June 26, 2020, from https://www.lshtm.ac.uk/newsevents/events/causal-inference-isnt-what-you-think-it

Long read: Cultural evolution, Covid-19, and preparing for what’s next. (2020, April 22). LSE Business Review. https://blogs.lse.ac.uk/businessreview/2020/04/22/long-read-cultural-evolution-covid-19-and-preparing-for-whats-next/

Friston KJ, Parr T, Zeidman P et al. Dynamic causal modelling of COVID-19 [version 1; peer review: awaiting peer review]. Wellcome Open Res 2020, 5:89 (https://doi.org/10.12688/wellcomeopenres.15881.1)

Marshall, B. D. L., & Galea, S. (2015). Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology. American Journal of Epidemiology, 181(2), 92–99. https://doi.org/10.1093/aje/kwu274

Lagnado, D. (2020 April 27). What if.... Changing Minds. https://www.youtube.com/watch?v=2W11CRLjRgo&app=desktop

Johnson, S. G. B., Bilovich, A., & Tuckett, D. (2020, April 30). Conviction Narrative Theory: A Theory of Choice Under Radical Uncertainty. Retrieved from psyarxiv.com/urc96

Intro to "Agential Intra-Action"

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.