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| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Dates (if different) | Conference Fee | Description |
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|[Neglected Assumptions in Causal Inference Workshop](https://sites.google.com/view/naci2021/home)| [ICML 2021](https://icml.cc/) | Daniel |23.7.2021 | 18.-24.7.2021 | 25$/100$ | As causality enjoys increasing attention in various areas of machine learning, this workshop turns the spotlight on the assumptions behind the successful application of causal inference techniques. |
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|[8th Causal Inference Workshop at UAI](https://sites.google.com/uw.edu/causaluai2021/home?authuser=0)| [UAI 2021](https://www.auai.org/uai2021/) | Daniel |30.7.2021 | 27.-30.7.2021 | free/25$ | The mission of this workshop is to encourage communication and exchange of ideas between researchers working in many branches of causal inference. We expect the workshop to cover an array of the latest methodological and applied research in causal inference. We intend to keep a balance in terms of applied and theoretical contributions. |
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|[15th Bayesian Modelling Applications Workshop at UAI](http://abnms.org/uai2021-apps-workshop/)| [UAI 2021](https://www.auai.org/uai2021/) | Daniel |30.7.2021 | 27.-30.7.2021 | free/25$ | The Bayesian Modelling Applications Workshop has become a focused forum for interchange among those interested in real world applications of graphical models and Bayesian networks.|
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|[Causal Discovery Workshop](http://nugget.unisa.edu.au/CD2021/index.html)| [KDD 2021](https://kdd.org/kdd2021) | Daniel |14.8.2021 | 14.-18.8.2021 | 50$/250$ | Following the success of CD 2016 - CD 2020, CD 2021 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets. |
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|[Bayesian Causal Inference for Real World Interactive Systems](https://bcirwis2021.github.io/index.html)| [KDD 2021](https://kdd.org/kdd2021) | Daniel |14.-15.8.2021 | 14.-18.8.2021 | 50$/250$ | The Bayesian approach is often depicted as being a principled means to combine information from different sources, however in causal production settings it is often not applied. In this workshop we consider if this is because the Bayesian paradigm is simply ill-suited to this causal setting. Does causality simply render the arguments in favor of the Bayesian paradigm irrelevant? |
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|[Causal Data Science Meeting 2021](https://causalscience.org/blog/call-for-papers-2021)|-|Daniel|15.-16.11.2021|-|free|Check out last year's edition below for more info, the format will be identical.|
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|[Causal Inference & Machine Learning: Why now?](https://why21.causalai.net/)| [NeurIPS 2021](https://nips.cc/Conferences/2021/Dates) | Daniel | 13.12.2021 | 6.-14.12.2021 | probably 25$/100$ | Workshop about how to combine causality and current machine learning approaches. There has been a similar workshop at NeurIPS in 2019.|
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Also check out this [Github Repo](https://jackietseng.github.io/conference_call_for_paper/conferences.html) for more conferences, e.g. AAAI and ICLR had interesting causality papers.
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**Past:**
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| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Fee | Description |
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|:------------------:|:--------:|:--------:|:------------:|:-----------:|:--------:|
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|[Causal Discovery Workshop](http://nugget.unisa.edu.au/CD2021/index.html)| [KDD 2021](https://kdd.org/kdd2021) | Daniel |14.8.2021 | 14.-18.8.2021 | 50$/250$ | Following the success of CD 2016 - CD 2020, CD 2021 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets. |
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|[Bayesian Causal Inference for Real World Interactive Systems](https://bcirwis2021.github.io/index.html)| [KDD 2021](https://kdd.org/kdd2021) | Daniel |14.-15.8.2021 | 14.-18.8.2021 | 50$/250$ | The Bayesian approach is often depicted as being a principled means to combine information from different sources, however in causal production settings it is often not applied. In this workshop we consider if this is because the Bayesian paradigm is simply ill-suited to this causal setting. Does causality simply render the arguments in favor of the Bayesian paradigm irrelevant? |
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|[8th Causal Inference Workshop at UAI](https://sites.google.com/uw.edu/causaluai2021/home?authuser=0)| [UAI 2021](https://www.auai.org/uai2021/) | Daniel |30.7.2021 | 27.-30.7.2021 | free/25$ | The mission of this workshop is to encourage communication and exchange of ideas between researchers working in many branches of causal inference. We expect the workshop to cover an array of the latest methodological and applied research in causal inference. We intend to keep a balance in terms of applied and theoretical contributions. |
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|[15th Bayesian Modelling Applications Workshop at UAI](http://abnms.org/uai2021-apps-workshop/)| [UAI 2021](https://www.auai.org/uai2021/) | Daniel |30.7.2021 | 27.-30.7.2021 | free/25$ | The Bayesian Modelling Applications Workshop has become a focused forum for interchange among those interested in real world applications of graphical models and Bayesian networks.|
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|[Neglected Assumptions in Causal Inference Workshop](https://sites.google.com/view/naci2021/home)| [ICML 2021](https://icml.cc/) | Daniel |23.7.2021 | 18.-24.7.2021 | 25$/100$ | As causality enjoys increasing attention in various areas of machine learning, this workshop turns the spotlight on the assumptions behind the successful application of causal inference techniques. |
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|[Causal Discovery and Causality-Inspired Machine Learning](https://neurips.cc/virtual/2020/protected/workshop_16110.html) | NeurIPS 2020 | Daniel | 11.12.2020 | 25$/100$ | Talks by some of the most respected researchers in the field. Most of them can be accessed over links in the schedule, but a few (e.g. the great keynotes by Clark Glymour and Caroline Uhler) have to be searched for manually in the recording of the livestream. The [overall schedule](https://neurips.cc/virtual/2020/protected/cal_main.html) of the conference also contains some causality related material, e.g. the Breiman lecture on causal learning by Marloes Maathuis.|
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|[Causal Data Science Meeting 2020](https://causalscience.netlify.app/programme/about/)|-|Daniel|11.-12.11.2020|free|Many causality enthusiasts from academia and industry presented their work in short talks. The [program](https://causalscience.netlify.app/programme/day-1/) gives a good overview of the topics and the keynotes have been made available on this [review article](https://causalscience.org/blog/thank-you-everyone) about the event. The organizers have recently started a [practitioners-oriented blog](https://causalscience.org/) that you might want to check out.| |
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