CORNELL CAUSAL READING GROUP

This site houses Cornell's Causal Reading Group, a student-led discussion space for PhD students interested in causality and causal inference.

       

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FALL 2023 SCHEDULE


LOGISTICS

WhenEvery Thursday at 3–4pm ET.
Start date7 September 2023.
End date30 November 2023.
Cancelled dates12 October & 23 November 2023.
Slack space#causal channel on the Cornell CIS Slack. Email maasch@cs.cornell.edu for assistance.
In-person (NYC)Bloomberg 081, Cornell Tech.

PAST TALKS

09.07 Jacqueline Maasch (Cornell CS)A brief overview of causal discovery[slides]
09.14 Brian Cho (Cornell ORIE)Policy evaluation in adaptive experiments
09.21 Ian Lundberg (Cornell IS)Principal stratification
09.28 Miruna Oprescu (Cornell CS)Uncertainty quantification in causal inference[paper]
10.05 Roshni Sahoo (Stanford CS)Policy Learning under Biased Sample Selection[paper] [slides]
10.19 Shantanu Gupta (CMU CS)Efficient Online Estimation of Causal Effects[paper]
10.26 Marianne Arriola (Cornell CS)Global Counterfactual Explainer for Graph Neural Networks[paper]
11.02 Alan Wang (Cornell ECE)Nonparametric Causal Representation Learning[paper]
11.09 Brian Cho (Cornell ORIE)Kernel Debiased Plug-in Estimation[paper] [slides]
11.16 Jacqueline Maasch (Cornell CS)Local Discovery by Partitioning[paper] [slides]
11.30 Andrew Jesson (Columbia CS)On Cautious Interaction[paper]

FAQs

Who can join? We welcome doctoral students, faculty, postdocs, and research staff from all disciplines at Cornell University's Ithaca campus, Weill Cornell Medicine, and Cornell Tech. Our members span multiple departments, including (but not limited to) Computer Science, Information Science, Statistics, and Operations Research.

How do I join? Please join the #causal channel on the Cornell CIS Slack workspace. All group announcements are made there. Please contact maasch@cs.cornell.edu if you have trouble joining the Slack channel.

Does this group cover applied research? The research interests of this group are broad. We welcome discussions on both theoretical and applied works. Any theoretical area is welcome, as is any application area. If it involves causality and causal inference, it's relevant.

Are meetings remote or in person? All meetings have a remote option through Zoom. Links are provided above and in the #causal channel on the Cornell CIS Slack workspace. A room has also been booked in NYC for optional in-person attendance. See the logistics above for location information, which is subject to change.

Can I join if I am not a member of the Cornell community? Please contact maasch@cs.cornell.edu if you are not affiliated with Cornell and you are interested in joining.

How do I volunteer to give a talk? The sign-up sheet can be accessed here. Please add your name, affiliation, topic, and relevant resources. Please contact maasch@cs.cornell.edu if you are not affiliated with Cornell CIS and you are interested in delivering a talk or moderating a discussion.

What if I am new to causality and causal inference? You are absolutely welcome! We provide suggested resources below for those who are new to this research area and would like to contribute to our group.

SUGGESTED RESOURCES

For those new to causal inference: We recommend starting with Brady Neal's Introduction to Causal Inference, which provides a textbook and recorded lectures.

Suggested textbooks:

  1. Jonas Peters, Dominik Janzing, & Bernhard Scholkopf. Elements of causal inference: foundations and learning algorithms. (The MIT Press, 2017).
  2. Hernán, M. A. & Robins, J. M. Causal Inference: What If. (2020).

Updated Fall 2023. This website was adapted from this source code.