|
|
**Upcoming:**
|
|
|
- There will be a 2021 edition of the **Causal Data Science Meeting** (see below). More information will be posted here.
|
|
|
|
|
|
| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Dates (if different) | Conference Fee | Description |
|
|
|
|:------------------:|:--------:|:--------:|:------------:|:-----------:|:--------:|:--------:|
|
|
|
|[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. |
|
|
|
|[Causal Data Science Meeting 2021](https://causalscience.org/)|-|Daniel|??|??|free|Not available yet, check out last year's edition below for more info.|
|
|
|
|??| [NeurIPS 2021](https://nips.cc/Conferences/2021/Dates) | Daniel | probably last 2 days | 6.-14.12.2021 | probably 25$/100$ | Not available yet, hopefully there will be a workshop similar to last year's edition.|
|
|
|
|
|
|
|
|
|
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.
|
|
|
|
|
|
**Past:**
|
|
|
- The **NeurIPS 2020 Workshop** [_Causal Discovery and Causality-Inspired Machine Learning_](https://neurips.cc/virtual/2020/protected/workshop_16110.html) featured 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.
|
|
|
- Maastricht University School of Business and Economics and Copenhagen Business School hosted the **Causal Data Science Meeting 2020** where 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. |
|
|
\ No newline at end of file |
|
|
|
|
|
| Title | Conference (if different) | Contributor | Date (DD.MM.YYYY) | Conference Fee | Description |
|
|
|
|:------------------:|:--------:|:--------:|:------------:|:-----------:|:--------:|
|
|
|
|[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.|
|
|
|
|[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.| |
|
|
\ No newline at end of file |