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On Data Manifolds Entailed by Structural Causal Models
Ricardo Dominguez-Olmedo · Amir-Hossein Karimi · Georgios Arvanitidis · Bernhard Schölkopf

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #638

The geometric structure of data is an important inductive bias in machine learning. In this work, we characterize the data manifolds entailed by structural causal models. The strengths of the proposed framework are twofold: firstly, the geometric structure of the data manifolds is causally informed, and secondly, it enables causal reasoning about the data manifolds in an interventional and a counterfactual sense. We showcase the versatility of the proposed framework by applying it to the generation of causally-grounded counterfactual explanations for machine learning classifiers, measuring distances along the data manifold in a differential geometric-principled manner.

Author Information

Ricardo Dominguez-Olmedo (Max Planck Institute for Intelligent Systems, Tübingen)
Amir-Hossein Karimi (University of Waterloo)

Amir-Hossein Karimi is a final-year PhD candidate at ETH Zurich and the Max Planck Institute for Intelligent Systems, working under the guidance of Prof. Dr. Bernhard Schölkopf and Prof. Dr. Isabel Valera. His research interests lie at the intersection of causal inference, explainable AI, and program synthesis. Amir's contributions to the problem of algorithmic recourse have been recognized through spotlight and oral presentations at top venues such as NeurIPS, ICML, AAAI, AISTATS, ACM-FAccT, and ACM-AIES. He has also authored a book chapter and a highly-regarded survey paper in the ACM Computing Surveys. Supported by the NSERC, CLS, and Google PhD fellowships, Amir's research agenda aims to address the need for systems that make use of the best of both human and machine capabilities towards building trustworthy systems for human-machine collaboration. Prior to his PhD, Amir earned several awards including the Spirit of Engineering Science Award (UofToronto, 2015) and the Alumni Gold Medal Award (UWaterloo, 2018) for notable community and academic performance. Alongside his education, Amir gained valuable industry experience at Facebook, Google Brain, and DeepMind, and has provided >$250,000 in AI-consulting services to various startups and incubators. Finally, Amir teaches introductory and advanced topics in AI to an online community @PrinceOfAI.

Georgios Arvanitidis (Technical University of Denmark)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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