Causal Inference Lab

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 researchers 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 one of the organizers of the reading group, Dean McHugh ( or Evan Iatrou (


Reading group: Elements of Causal Inference

Next meeting
 Monday, March 21, 2021. 14:05-15:30 CET. Biweekly sessions.

Reading: Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. 2017. Elements of Causal Inference: Foundations and Learning Algorithms. The MIT Press. [pdf]

For the next meeting, we have to

  1. read from Chapter 6.6 "Calculating Intervention Distributions by Covariate Adjustment" to Chapter 6.11 "Algorithmic Independence of Conditionals"
  2. work on the exercises of Chapter 6.12 "Problems". To be consistent with the book, for programming language choose R. If you want others to look at your solutions before the meeting, you can upload them to our Google Drive file [solutions].
  3. work on the following exercises left unanswered during our last meeting:
    • p.83: Why {C,G} are d-separated by {X} and not by {X,H}? How exactly will the negation of d-separation look like? 
    • p.227 (Proof of Proposition 6.13): Why (C.1) is the case? 

Location: Hybrid form: Room A1.14 SP 904 and online via zoom [reoccurring link].

Structure: There are no main speakers. Participants are expected -but not obliged- to contribute to the following: 

  1. Discuss the solutions to the problems and leftover questions/points from the previous meetings
  2. Discuss current week's reading:
    • Questions/Objections to points raised by the authors
    • Clarifications of points that we understand, but they are written obscurely
    • Highlighting points we deem important and why we consider them as such

Suggested readings:  These are readings that are mentioned during our meetings or that are relevant to the topics raised. Feel free to contribute to the list. 

  • Alternative semantics for causal inference seemingly more intuitive for statistics/economics:   
    Dawid, Philip. 2021. "Decision-theoretic foundations for statistical causality." Journal of Causal Inference 9 (1): 39-77.
  • Intersections among algorithmic information, physics, and causality:
    Janzing, Dominik, Rafael Chaves, and Bernhard Schölkopf. 2016. "Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference." New Journal of Physics 18 (9):  093052.
  • Connections between structural causal models and physical models:  
    Mooij, Joris M., Dominik Janzing, and Bernhard Scholkopf. 2013. “From Ordinary Differential Equations to Structural Causal Models: The Deterministic Case.” In Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence,440–448. UAI’13. Bellevue, WA: AUAI Press. [pdf]

Background Readings: Literature that may help you comprehend better the biweekly readings.

  • James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An introduction to statistical learning: with applications in R. 2nd ed. Springer. [1st edition - free download]
  • Pearl, Judea. 2009. Causality. 2nd ed. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511803161.
  • Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. 2016. Causal Inference in Statistics. Wiley. 

 For any questions, contact Evan.

Previous meetings 

Monday 08 November 2021, 13:00-14:00

  • Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf (2017), Elements of Causal Inference: Foundations and Learning Algorithms: Chapters 1,2,3.  [pdf]

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]

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

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

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

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

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

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

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/]

  • 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

Monday 12 October 2020

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/]

Friday 26 June 2020

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

Friday 12 June 2020

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

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

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

Friday 13 March 2020 

Friday 28 February 2020

10 January 2020

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

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

11 November 2019 

28 October 2019: Hierarchal causal learning

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

20 May 2019: Visit by Sander Beckers

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.

8 April 2019: Causal learning using temporal information

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

4 March 2019: Bayesian algorithims in causal inference

18 February 2019: Causal modelling semantics of counterfactuals


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/]
  • Alicke, M. D., Rose, D., & Bloom, D. (2011). Causation, norm violation, and culpable control. The Journal of Philosophy, 108(12), 670-696 [url] [preprint]

Further reading materials

The Book of Why


Causal Inference in Statistics

Actual Causality

Causation, Prediction, and Search