Causality Algorithms Reading Group Robust

Causality & Algorithms Reading Group -- Robust identifiability of linear SEM's

Karthik Abinav Sankaraman (Facebook) presented his work studying identifiability of linear structural equation models...

Causality & Algorithms Reading Group -- Efficient Intervention Design for Causal Discovery

Raghav Addanki (UMass Amherst) speaks about his work: https://arxiv.org/abs/2005.11736.

Causal Representation Learning

Presenter: Chaochao Lu, Unviersity of Cambridge Abstract: In recent years, there is growing interest in integrating...

Causality & Algorithms Reading Group -- Identification of Causal Effects

Saravanan Kandasamy (https://scholar.google.com/citations?user=SFQyZMsAAAAJ&hl=en) on an

Invariance, Causality and Novel Robustness

Peter Bühlmann (ETH Zürich) https://simons.berkeley.edu/talks/invariance-

Causality & Algorithms Reading Group -- Recent work in truncated statistics

Andrew Ilyas (MIT) gave a survey about recent work in statistical inference from truncated samples.

Algorithmic Recourse Through a Causal Lens: Amir-Hossein Karimi and Julius von Kuegelgen

Slides: https://drive.google.com/file/d/1Lk2z82QwwnKqr3IIZ6H7-FZZip4gR47D/view?usp=sharing Presentation and...

Daniel Malinsky: Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

"Explaining the Behavior of Black-Box Prediction

15. Causality || Probabilistic ML Reading Group

Fifteenth session of the Probabilistic ML

Causal Identification under Markov Equivalence [Causal Programming Reading Group]

Causal reading group

OAMLS -- Causality and Distributional Robustness Part 1 -- Jonas Peters, Sorawit Saengkyongam

Causality

Edward Kennedy: Optimal doubly robust estimation of heterogeneous causal effects

"Optimal doubly

Causal ML Paper Reading Group - 2020-06-20

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