juangamella
PhD Student @ Seminar for Statistics, ETH Zürich. Intern @ Apple Health AI
ETH ZürichZürich, Switzerland
Pinned Repositories
abcd
Code to reproduce the results comparing ABCD to A-ICP in the paper "Active Invariant Causal Prediction: Experiment Selection through Stability", by Juan L Gamella and Christina Heinze-Deml.
aicp
Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", by Juan L Gamella and Christina Heinze-Deml.
causal-chamber
Dataset repository for the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
causal-chamber-paper
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
ges
Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Search" by David Maxwell Chickering.
gies
Python implementation of the GIES algorithm for causal discovery, from the 2012 paper "Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs" by Alain Hauser and Peter Bühlmann.
gnies
Python implementation of the GnIES algorithm from the 2022 paper "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions" by Gamella et al.
icp
Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant prediction: identification and confidence intervals" by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.
sempler
Framework to generate observational and interventional samples from structural equation models (SEMs)
ut-lvce
Python implementation of the UT-LVCE algorithms from the 2022 paper "Perturbations and Causality in Gaussian Latent Variable Models", by A. Taeb, JL. Gamella, C. Heinze-Deml and P. Bühlmann.
juangamella's Repositories
juangamella/ges
Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Search" by David Maxwell Chickering.
juangamella/causal-chamber
Dataset repository for the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
juangamella/aicp
Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", by Juan L Gamella and Christina Heinze-Deml.
juangamella/icp
Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant prediction: identification and confidence intervals" by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.
juangamella/sempler
Framework to generate observational and interventional samples from structural equation models (SEMs)
juangamella/causal-chamber-paper
Code to reproduce the case studies of the 2024 paper "The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology" by Juan L. Gamella, Jonas Peters and Peter Bühlmann.
juangamella/abcd
Code to reproduce the results comparing ABCD to A-ICP in the paper "Active Invariant Causal Prediction: Experiment Selection through Stability", by Juan L Gamella and Christina Heinze-Deml.
juangamella/gies
Python implementation of the GIES algorithm for causal discovery, from the 2012 paper "Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs" by Alain Hauser and Peter Bühlmann.
juangamella/ut-lvce
Python implementation of the UT-LVCE algorithms from the 2022 paper "Perturbations and Causality in Gaussian Latent Variable Models", by A. Taeb, JL. Gamella, C. Heinze-Deml and P. Bühlmann.
juangamella/gnies
Python implementation of the GnIES algorithm from the 2022 paper "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions" by Gamella et al.
juangamella/stars
Python implementation of the StARS algorithm: "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" by Han Liu, Kathryn Roeder, Larry Wasserman, 2010
juangamella/vistools
A signal-processing and visualization suite written in C and OpenGL
juangamella/gnies-paper
Experiments repository for the 2022 paper "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions" by Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml and Peter Bühlmann.
juangamella/slate
Beautiful static documentation for your API
juangamella/ut-lvce-paper
Experiments repository for the paper "Perturbations and Causality in Gaussian Latent Variable Models", by A. Taeb, JL. Gamella, C. Heinze-Deml and P. Bühlmann.