Reading Group

The Causal Inference Lab hosts a biweekly reading group to discuss recent advances in the field of causal inference, from both empirical and formal perspectives. Everyone with an interest in discussing causal inference is very welcome to come along. We also host talks by reserachers studying causal inference.

If you would like to receive updates about the Causal Inference Lab reading group, or would like to present your research at the Causal Inference Lab, please contact the organizer of the reading group, Dean McHugh.

 

Upcoming meetings

Monday 17 May 2021, 13:00 - 14:00

• Benjamin Rottman & Reid Hastie (2016), Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away [pdf] [doi]
• Zoom link https://uva-live.zoom.us/j/81813222366

Wednesday 2 June 2021, 13:00 - 16:00

• Reading to be announced

Wednesday 16 June 2021, 13:00 - 16:00

• Reading to be announced

Previous meetings 

Monday 3 May 2021, 15:00 - 16:00

• Joshua Tenebaum (1999), Bayesian modeling of human concept learning [pdf]
• Joshua Tenebaum (2000), Rules and similarity in concept learning [pdf]

Monday 19 April 2021, 12:30 - 13:30

• Denise Cummins (1995), Naive theories and causal deduction [doi:10.3758/BF03197265]

Monday 29 March 2021, 15:30 - 16:30

• Skovgaard-Olsen, Stephan & Waldmann (preprint), Conditionals and the Hierarchy of Causal Queries [pdf]

Monday 15 March 2021, 10:00 - 11:30

• Talk by Niels Skovgaard-Olsen (Göttingen), Conditionals and the hierarchy of causal queries. For more information please click here.

Monday 1 March 2021, 13:30 - 14:30

• Jern, Derrow-Pinion & Piergiovanni (preprint), A computational framework for understanding the roles of simplicity and rational support in people’s behavior explanations [doi:10.31234/osf.io/fuwst] [pdf]
• Zoom link: https://uva-live.zoom.us/j/81795545403

Monday 15 February 2021, 13:30 - 14:30

•  Morris, Phillips, Huang & Cushman (preprint), Generating options and choosing between them rely on distinct forms of value representation [doi:10.31234/osf.io/2ayw3] [pdf]
• Zoom link: https://uva-live.zoom.us/j/86544722759

Monday 1 February 2021, 12:30-13:00

• Danks (2014), Unifying the Mind, chapter 6 [pdf of chapter 6] [pdf of whole book]

Tuesday 26 January 2021, 14:30-15:00 at Bias Barometer Group

• Talk by P. Solanki at Bias Barometer group on judgements of social groups. For more information please click https://bias-barometer.github.io/seminars_posts/judgement-of-social-groups/.

Monday 18 January 2021, 13:30-14:30

• Phillips, J., Morris, A., & Cushman, F. (2019). How we know what not to think. Trends in cognitive sciences, 23(12), 1026-1040. https://doi.org/10.1016/j.tics.2019.09.007 [pdf]

Monday 21 December 2020, 15:00-16:00

• Kirfel & Lagnado (preprint), Causal judgments about atypical actions are influenced by agents’ epistemic states [doi:10.31234/osf.io/yvstb]

• Alike (1992), Culpable causation [link] [pdf]

Monday 7 December 2020, 16:00-17:00

• Discussing results of experiment replicating Icard et al. (2017)

Monday 23 November 2020, 15:00-16:00

• Discussing results of experiment replicating Icard et al. (2017)

Monday 9 November 2020

• Presentation by Laura Vetter and Simone Astarita, How do moral norms influence causal judgements?

Monday 26 October 2020

• Kirfel & Lagnado (2018), Statistical norm effects in causal cognition https://cogsci.mindmodeling.org/2018/papers/0132/0132.pdf

Monday 12 October 2020

• Michael Tessler & Noah Goodman (preprint), Learning from Generic Language [doi:10.31234/osf.io/hnm8p]

Monday 28 September 2020

• Ciara Willett & Benjamin Rottman (2019), The accuracy of causal learning over 24 days [pdf] 

Friday 19 June 2020

• Tobias Gerstenberg, Noah Goodman, David Lagnado & Joshua Tenenbaum (submitted), A counterfactual simulation model of causal judgment [preprint doi:10.31234/osf.io/7zj94]

Friday 26 June 2020

• Tobias Gerstenberg, Noah Goodman, David Lagnado & Joshua Tenenbaum (submitted), A counterfactual simulation model of causal judgment [preprint doi:10.31234/osf.io/7zj94]

Friday 12 June 2020

• Tobias Gerstenberg, Noah Goodman, David Lagnado & Joshua Tenenbaum (submitted), A counterfactual simulation model of causal judgment [preprint doi:10.31234/osf.io/7zj94]

Friday 8 May 2020: Causal Inference Day

• 16:00-17:30: MLC Seminar presented by members of the Causal Inference Lab

Friday 24 April 2020

• Eric Hiddleston (2005), Causal powers [doi:10.1093/phisci/axi102]

Friday 10 April 2020

• Jonathan Phillips, Jamie Luguri & Joshua Knobe (2015), Unifying morality’s influence on non-moral judgments: The relevance of alternative possibilities [doi:10.1016/j.cognition.2015.08.001]

Friday 27 March 2020

• Christopher Hitchcock & Joshua Knobe (2009), Cause and norm doi:10.5840/jphil20091061128 [preprint]

• Julia Driver (2008), Attributions of causation and moral responsibility. Moral Psychology, 2, 423–440 [copy of book chapter]

Friday 13 March 2020 

• Thomas Icard, Jonathan Kominsky & Joshua Knobe (2017), Normality and actual causal strength doi:10.1016/j.cognition.2017.01.010 [preprint]

Friday 28 February 2020

• Laura Franklin-Hall, Explaining causal selection with explanatory causal economy doi.org/10.1007/978-94-017-9822-8_18 (click here for an open access version)

10 January 2020

• Paul Muentener & Elizabeth Bonawitz (2017), The Development of Causal Reasoning doi/org.10.1093/oxfordhb/9780199399550.013.40 [preprint]

9 December 2019

• 09:30-10:30, F2.01 PhD Meeting room

• Nadya Vasilyeva, Thomas Blanchard & Tania Lombrozo (2018), Stable Causal Relationships Are Better Causal Relationships doi.org/10.1111/cogs.12605

25 November 2019

• 09:30-10:30, Oude Turfmarkt 143, room 1.13 (Katrin's office)

• Tania Lambrozo (2010), Causal–explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions https://doi.org/10.1016/j.cogpsych.2010.05.002

11 November 2019 

• 09:30-10:30, F2.01 PhD Meeting room
• Judea Pearl (2009), Causality 2nd edition, chapter 7 https://doi.org/10.1017/CBO9780511803161.009

28 October 2019: Hierarchal causal learning

• 14:00-15:00, F1.15 ILLC Seminar Room
• Johnson and Keil (2014), Causal inference and the hierarchical structure of experience. http://dx.doi.org/10.1037/a0038192 [open access pdf]

1 July 2019: Presentations by PhD students II

• Ivar Kolvoort, Experimental results on causal inference

17 June 2019: Presentations by PhD students I

• Dean McHugh, Causality in dynamical systems [slides]
• Kaibo Xie, Backtracking and counterfactual reasoning [slides]

3 June 2019: The psychology of modality

• Jonathan Phillips & Joshua Knobe (2018), The psychological representation of modality https://doi.org/10.1111/mila.12165 [open access pdf]

20 May 2019: Visit by Sander Beckers

• Sander Beckers & Joost Vennekens (2018), A principled approach to defining actual causation.
  https://doi.org/10.1007/s11229-016-1247-1 [open access pdf]
• Sander Beckers and Joost Vennekens (2017), The transitivity and asymmetry of actual causation. http://dx.doi.org/10.3998/ergo.12405314.0004.001
• Sander Beckers' slides

6 May 2019: Comparing possible worlds and causal modelling semantics of counterfactuals

• Kok Yong Lee (2015), Motivating the Causal Modeling Semantics of Counterfactuals, or, Why We Should Favor the Causal Modeling Semantics over the Possible-Worlds Semantics. https://doi.org/10.1007/978-3-662-48357-2_5

8 April 2019: Causal learning using temporal information

• Neil Bramley, Tobias Gerstenberg, Ralf Mayrhofer and David Lagnado (2018), Time in causal structure learning. http://dx.doi.org/10.1037/xlm0000548 [open access pdf]
• Zachary Davis, Neil Bramley and Bob Rehder (2018), Causal Structure Learning with Continuous Variables in Continuous Time. [open access pdf]

25 March 2019: Work by Samantha Kleinberg

• Samantha Kleinberg, Marco Antoniotti, Naren Ramakrishnan and Bud Mishra (2007), Modal Logic, Temporal Models and Neural Circuits: What Connects Them. [open access pdf]
• Samantha Kleinberg and Bud Mishra (2009), The Temporal Logic of Causal Structures
   [open access pdf]

11 March 2019: Interpretating of graphical causal models

• Judea Pearl (2019), On the interpretation of do(x). [open access pdf]
• Joseph Halpern (2017), Actual causation: What's the use? https://doi.org/10.1093/oso/9780198746911.003.0007
• Hitchcock (2012), Portable causal dependence: a tale of consilience. 
  https://doi.org/10.1086/667899

4 March 2019: Bayesian algorithims in causal inference

• Elizabeth Bonawitz, Stephanie Denison, Alison Gopnik and Thomas Griffith (2014), Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference. https://doi.org/10.1016/j.cogpsych.2014.06.003
• Adam Sanborn and Nick Chater (2016), Bayesian brains without probabilities. https://doi.org/10.1016/j.tics.2016.10.003

18 February 2019: Causal modelling semantics of counterfactuals

• Ray Briggs (2012), Interventionist counterfactuals. https://doi.org/10.1007/s11098-012-9908-5

 

Upcoming readings

• Benjamin Rottman & Reid Hastie (2014), Reasoning about causal relationships: Inferences on causal networks [pdf] [doi:10.1037/a0031903]
• Henne et al. (forthcoming), Norms affect prospective causal judgments [doi:10.31219/osf.io/2nwb4]
• Alicke, M. D., Rose, D., & Bloom, D. (2011). Causation, norm violation, and culpable control. The Journal of Philosophy, 108(12), 670-696 [url] [preprint]