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**Upcoming:**
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| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Dates (if different) | Conference Fee | Description |
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| [ICML 2022](https://icml.cc/) |-|Daniel| 17.-23.7.2022 |-|?|Perhaps there will be another causality workshop. Takes place in Maryland, USA, with "virtual elements".| paper submission already over; workshop notification: 16.3. |
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|[UAI](https://www.auai.org/uai2022/dates)|-|Daniel|1.-5.8.2022|-|?|Takes place in Eindhoven. paper submission: 25.2. ; workshop notification: 6.4.|
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|[SIGKDD](https://kdd.org/kdd2022/)|-|Daniel|14.18.8.2022|-|?|Takes place in Washington DC. paper submission: 10.2. ; workshop paper submission: 26.5.|
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|[NeurIPS 2022](https://nips.cc/Conferences/FutureMeetings)|-|Daniel|26.11.-4.12.2022|-|?|Takes place in New Orleans.|
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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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... | ... | @@ -15,6 +13,8 @@ Also check out this [Github Repo](https://jackietseng.github.io/conference_call_ |
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| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Fee | Description |
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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 | 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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|[Causal Data Science Meeting 2021](https://causalscience.org/blog/call-for-papers-2021)|-|Daniel|15.-16.11.2021|free|Again a very stimulating event for causality enthusiasts. The organizers' blog contains a short summary of the event.|
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|[Causal Discovery Workshop](https://nugget.unisa.edu.au/cd2021.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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