Resource Hub
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Mayo Clinic x causaLens: Towards Causal Analysis of Genetic Factors for Colorectal Cancer
Mayo Clinic and causaLens researchers leveraged Causal AI techniques and […]
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Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data
causaLens’ NeurIPS 2020 paper sets out a framework for benchmarking […]
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Domain Knowledge in A*-Based Causal Discovery
Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While…
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Unsuitability of NOTEARS for Causal Graph Discovery
Many popular causal discovery algorithms have significant limitations in applied […]
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An Overview of the Methodologies of Causal Discovery
Until recently, discovering cause-and-effect relationships involved conducting a carefully controlled […]
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Equality of Effort via Algorithmic Recourse
AI systems are increasingly used in many socially significant applications, such as loan approval, hiring decisions, legal processes, and healthcare,…
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Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
Our Analysis of the TOPCAT Trial: Complex trials with heterogeneities […]
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A Causal Analysis of Harm
Defining harm is essential for dealing with the many legal and regulatory issues around the growing integration of autonomous systems…
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On Testing for Discrimination Using Causal Models
causaLens’ own Hana Chockler in collaboration with Cornell’s Joe Halpern […]
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Explanations for Occluded Images
causaLens Principal Investigator Hana Chockler’s research paper “Explanations for Occluded […]
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Ranking Policy Decisions
causaLens’ NeurIPS paper introduces a novel method based on root […]
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