Causality Reading Group

Syracuse

"I would rather discover one true cause than gain the kingdom of Persia. "

Democritus

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Weekly meeting schedule

Week 9

Time: 1 PM, Tuesday 21st of August

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our ninth meeting we will discuss independence tests and attempt our own solution for the Old Faithful example as mentioned in "Nonlinear causal discovery with additive noise models" by Hoyer et al. (link: https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models)

Week 8

Time: 1 PM, Tuesday 7th of August

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our eighth meeting we will discuss HSICs and walk through the code for the Old Faithful example as mentioned in "Nonlinear causal discovery with additive noise models" by Hoyer et al. (link: https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models)

Week 7

Time: 1 PM, Tuesday 31st of July

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our seventh meeting we will discuss "Nonlinear causal discovery with additive noise models" by Hoyer et al. (link: https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models)

Week 6

Time: 1 PM, Tuesday 24th of July

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our sixth meeting we will discuss the assumptions for Structural Identifiability in Section 4.1.1 to 4.1.4 (page 43-52) of “Elements of Causal Inference” (link: https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0) by Bernhard Schölkopf, Jonas Peters and Dominik Janzing.

Week 5

Time: 1 PM, Tuesday 17th of July

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our fifth meeting we will have an in-depth discussion of Problem 3.7 of “Elements of Causal Inference” (link: https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0) by Bernhard Schölkopf, Jonas Peters and Dominik Janzing.

Week 4

Time: 10AM, Tuesday 10th of July

Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our fourth meeting we will play with some hands on problems at the end of chapter 3 and briefly discuss the assumptions for causal inference in Chapter 2 of “Elements of Causal Inference” (link: https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0) by Bernhard Schölkopf, Jonas Peters and Dominik Janzing.

Week 3

Time: 11AM, MONDAY 2nd of July Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our third meeting we will the assumption for causal inference in Chapter 2 of “Elements of Causal Inference” (link: https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0) by Bernhard Schölkopf, Jonas Peters and Dominik Janzing. The book is a machine learning approach to causal inference.

UPDATE: What we ended up doing: Discuss the Eye desease example from chapter 3

Week 2

Time: 10am, Tuesday 26th of June Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

In our second meeting we will discuss the causality principles and its two examples (Section 1.3 and 1.4) in the introduction of “Elements of Causal Inference” (link: https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0) by Bernhard Schölkopf, Jonas Peters and Dominik Janzing. The book is a machine learning approach to causal inference.

Week 1

Time: 10am, Tuesday 19th of June Location: 4-206A (LSC, Computer Science Department, glass room out of elevator on the left)

The Causality Reading Group will meet for its first meeting this summer. We will discuss the last page of the short 4-page paper on “Statistics for big data: A perspective” by Peter Bühlmannand and Sara van de Geer, ETH Zürich. (attached, link: https://www.sciencedirect.com/science/article/pii/S0167715218300610) Over the course of the summer, we will discuss questions such as: What is causality? How can we identify causality? Can we justify causal statements? Papers, opinions and speakers will come from Philosophy, Computer Science and Mathematics.