Schedule

Overview of the topics presented and discussed at the reading group:

Date Title Citation Category Presenter

06.05.2021 Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates $\rightarrow$ [Keith2020] [@Keith2020] Causal Inference, NLP Roman 29.04.2021 A Crash Course in Good and Bad Controls [@cinelli2020crash] Causal Inference Michael 22.04.2021 Mechanisms, Modularity and Constitutive Explanation [@Kuorikoski2012MechanismsMA] Representations Houssam 15.04.2021 Learning Representations of Semantic Concepts and their Causal Relationships Video, Slides Representations, ML, NLP Philipp 01.04.2021 Towards Causal Representation Learning $\rightarrow$ [schoelkopf2021towards] [@scholkopf2021toward] Causality and Machine Learning Georgios 25.03.2021 Causal inference and the data-fusion problem $\rightarrow$ [Bareinboim2016] [@Bareinboim2016] Causal Inference Roman 18.03.2021 Directions for explainable knowledge-enabled systems [@chari2020directions] Interpretable ML João 11.03.2021 Practice of Epidemiology Directed Acyclic Graphs, Sufficient Causes, and the Properties of Conditioning on a Common Effect [@Vanderweele2007] Causal Discovery Houssam 04.03.2021 Understanding Simpson’s Paradox [@pearl2014comment] Causal Inference Philipp 25.02.2021 Connections between causality and machine learning [@PetersYouTube2017] Video Roman 18.02.2021 Modeling confounding by half-sibling regression$\rightarrow$ [schoelkopf2016] [@scholkopf2016modeling] Causality and Machine Learning Georgios 11.02.2021 Causality matters in medical imaging [@castro2020causality] Data generation process Michael 04.02.2021 Causal Effect Inference with Deep Latent-Variable Models $\rightarrow$ [Louizos2017] [@Louizos2017] Causal Inference Houssam 28.01.2021 A Survey of Learning Causality with Data (cont.) $\rightarrow$ [Guo2020] [@Guo2020] Survey João 21.01.2021 A Survey of Learning Causality with Data $\rightarrow$ [Guo2020] [@Guo2020] Survey Roman 14.01.2021 Review of Causal Discovery Methods Based on Graphical Models $\rightarrow$ [Glymour2019] [@Glymour2019] Survey Georgios

Backlog

Possible papers to read in the future, starting with the next paper in line first.

 **Title**                                                                                 **Citation**                  **Category**       **Presenter**

 Integrating Overlapping Datasets Using Bivariate Causal Discovery                         [@Dhir2019]                   Causal Discovery   
 Causal Interpretability for Machine Learning - Problems, Methods and Evaluation           [@Moraffah2020]               Machine Learning   
 Learning Counterfactual Representations for Estimating Individual Dose-Response Curves    [@schwab2020learning]         Causal Inference   
 Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising   [@bottou2012counterfactual]   Machine Learning

Template

Here templates to be filled out for each item being discussed.

Title of the paper

Why

Please provide a short motivation, why the paper has been selected.

Contribution

Please provide a single sentence on the main contribution of the paper.

Summary

Please provide a summary of the paper that can be directly copy’n’pasted into a paper.

Significance

The main strength of the paper.

Problems/Concerns

The main weakness of the paper.

Datasets & Algorithms

List of datasets, and/or algos being used. Also mention if the dataset is available (plus links), github

References

Which references are particularly interesting, which could be follow-up papers to discuss.

Recommendations

Recommendations by Georgios

Recommendations by João

Recommendations by Michael

Recommendations by Philipp