Causal Inference from Competing Treatments | Social Foundations of Computation – Max Planck Institute for Intelligent Systems

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An Engine Not a Camera: Measuring Performative Power of Online Search

Causal Inference from Competing Treatments

Contesting Algorithmic Systems

Algorithmic Collective Action in Machine Learning

Decline Now: A Combinatorial Model for Algorithmic Collective Action

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists

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Fairness and Machine Learning: Limitations and Opportunities

Unprocessing Seven Years of Algorithmic Fairness

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Training on the Test Task Confounds Evaluation and Emergence

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Social Foundations of Computation Members Publications

Causal Inference from Competing Treatments

Competing treatments — Under competition, an experimenter can only indirectly control which rank a treatment gets. Treatment at lower rank has a smaller, thus complicating causal inference.

We model a scenario where multiple experimenters compete over the same subject pool. Our work characterizes the optimal statistical estimator at equilibrium [].

Members

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Social Foundations of Computation

Ana-Andreea Stoica

Research Group Leader

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Social Foundations of Computation

Vivian Nastl

Doctoral Researcher

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Social Foundations of Computation

Moritz Hardt

Director

Publications

Social Foundations of Computation Conference Paper Causal Inference from Competing Treatments Stoica, A., Nastl, V. Y., Hardt, M. In Proceedings of the 41st International Conference on Machine Learning (ICML 2024), PMLR, The Forty-First International Conference on Machine Learning (ICML), July 2024 (Published) ArXiv URL BibTeX