5 лет назад совместно с «Всероссийской государственной библиотекой иностранной литературы имени М.И.Рудомино» я сделал рекомендательный список литературы и периодики, посвященных поведенческой экономике. Публикую его сегодня, следуя рекомендации, когда-то данной мне Быковским – спустя 5 лет, и сопровождая сегодняшними комментариями.
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| Name | Team | Software | Keywords | link |
| causalai | Salesforce | Python | Discovery | Time Series | basic-inference | https://github.com/salesforce/causalai |
| pycid | Causal Incentives Working Group a collaboration with Oxford, DeepMind and Toronto researchers | Python | bayesian-networks | influence-diagrams | https://github.com/causalincentives/pycid |
| causal-learn | Center for Causal Discovery, pittsbugh | Python | discovery |hidden-causal | Time Series basic-inference | tetrad | https://github.com/causalincentives/pycid |
| CausalImpact | Google, Inc | R | Bayesian time-series | https://github.com/google/CausalImpact |
| EconML | Microsoft Research | Python | Machine Learning Based Estimation of Heterogeneous Treatment Effects | https://econml.azurewebsites.net/index.html |
| causalnex | Petuum | Python | augment model | structure learning | https://causalnex.readthedocs.io/en/latest/index.html |
| dowhy | Microsoft Research | Python | Double ML | Matching | Balancing Synthetic control Outcome-based Learners (e.f. X-Learner) Dif&Dif | basic-inference | https://github.com/py-why/dowhy |
| causalToolbox | University of California | R | X-learner | https://github.com/forestry-labs/causalToolbox |
| showwhy | Microsoft Research | TypeScript | no-code interfaces | https://github.com/microsoft/showwhy |
| causalimages-software | The University of Texas at Austin | R | computer-vision | earth-observation biomedical-image-analysis | https://github.com/cjerzak/causalimages-software |
| gCastle | Huawei Noah's Ark Lab | Python | Structure Learning gradient-based learning | https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle |
| causallib | BiomedSciAI | Python | basic-inference | https://github.com/BiomedSciAI/causallib |
| causal-cmd | Center for Causal Discovery, pittsbugh | java | CMD based | discovery | https://bd2kccd.github.io/docs/causal-cmd/ |
| ResearchMap | UCLA | JS | Optimal experiments | augmented models | https://github.com/ResearchMaps/ |
5 лет назад совместно с «Всероссийской государственной библиотекой иностранной литературы имени М.И.Рудомино» я сделал рекомендательный список литературы и периодики, посвященных поведенческой экономике. Публикую его сегодня, следуя рекомендации, когда-то данной мне Быковским – спустя 5 лет, и сопровождая сегодняшними комментариями.
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
| Name | Team | Software | Keywords | link |
| causalai | Salesforce | Python | Discovery | Time Series | basic-inference | https://github.com/salesforce/causalai |
| pycid | Causal Incentives Working Group a collaboration with Oxford, DeepMind and Toronto researchers | Python | bayesian-networks | influence-diagrams | https://github.com/causalincentives/pycid |
| causal-learn | Center for Causal Discovery, pittsbugh | Python | discovery |hidden-causal | Time Series basic-inference | tetrad | https://github.com/causalincentives/pycid |
| CausalImpact | Google, Inc | R | Bayesian time-series | https://github.com/google/CausalImpact |
| EconML | Microsoft Research | Python | Machine Learning Based Estimation of Heterogeneous Treatment Effects | https://econml.azurewebsites.net/index.html |
| causalnex | Petuum | Python | augment model | structure learning | https://causalnex.readthedocs.io/en/latest/index.html |
| dowhy | Microsoft Research | Python | Double ML | Matching | Balancing Synthetic control Outcome-based Learners (e.f. X-Learner) Dif&Dif | basic-inference | https://github.com/py-why/dowhy |
| causalToolbox | University of California | R | X-learner | https://github.com/forestry-labs/causalToolbox |
| showwhy | Microsoft Research | TypeScript | no-code interfaces | https://github.com/microsoft/showwhy |
| causalimages-software | The University of Texas at Austin | R | computer-vision | earth-observation biomedical-image-analysis | https://github.com/cjerzak/causalimages-software |
| gCastle | Huawei Noah's Ark Lab | Python | Structure Learning gradient-based learning | https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle |
| causallib | BiomedSciAI | Python | basic-inference | https://github.com/BiomedSciAI/causallib |
| causal-cmd | Center for Causal Discovery, pittsbugh | java | CMD based | discovery | https://bd2kccd.github.io/docs/causal-cmd/ |
| ResearchMap | UCLA | JS | Optimal experiments | augmented models | https://github.com/ResearchMaps/ |
5 лет назад совместно с «Всероссийской государственной библиотекой иностранной литературы имени М.И.Рудомино» я сделал рекомендательный список литературы и периодики, посвященных поведенческой экономике. Публикую его сегодня, следуя рекомендации, когда-то данной мне Быковским – спустя 5 лет, и сопровождая сегодняшними комментариями.
| Name | Team | Software | Keywords | link |
| causalai | Salesforce | Python | Discovery | Time Series | basic-inference | https://github.com/salesforce/causalai |
| pycid | Causal Incentives Working Group a collaboration with Oxford, DeepMind and Toronto researchers | Python | bayesian-networks | influence-diagrams | https://github.com/causalincentives/pycid |
| causal-learn | Center for Causal Discovery, pittsbugh | Python | discovery |hidden-causal | Time Series basic-inference | tetrad | https://github.com/causalincentives/pycid |
| CausalImpact | Google, Inc | R | Bayesian time-series | https://github.com/google/CausalImpact |
| EconML | Microsoft Research | Python | Machine Learning Based Estimation of Heterogeneous Treatment Effects | https://econml.azurewebsites.net/index.html |
| causalnex | Petuum | Python | augment model | structure learning | https://causalnex.readthedocs.io/en/latest/index.html |
| dowhy | Microsoft Research | Python | Double ML | Matching | Balancing Synthetic control Outcome-based Learners (e.f. X-Learner) Dif&Dif | basic-inference | https://github.com/py-why/dowhy |
| causalToolbox | University of California | R | X-learner | https://github.com/forestry-labs/causalToolbox |
| showwhy | Microsoft Research | TypeScript | no-code interfaces | https://github.com/microsoft/showwhy |
| causalimages-software | The University of Texas at Austin | R | computer-vision | earth-observation biomedical-image-analysis | https://github.com/cjerzak/causalimages-software |
| gCastle | Huawei Noah's Ark Lab | Python | Structure Learning gradient-based learning | https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle |
| causallib | BiomedSciAI | Python | basic-inference | https://github.com/BiomedSciAI/causallib |
| causal-cmd | Center for Causal Discovery, pittsbugh | java | CMD based | discovery | https://bd2kccd.github.io/docs/causal-cmd/ |
| ResearchMap | UCLA | JS | Optimal experiments | augmented models | https://github.com/ResearchMaps/ |
5 лет назад совместно с «Всероссийской государственной библиотекой иностранной литературы имени М.И.Рудомино» я сделал рекомендательный список литературы и периодики, посвященных поведенческой экономике. Публикую его сегодня, следуя рекомендации, когда-то данной мне Быковским – спустя 5 лет, и сопровождая сегодняшними комментариями.
| Name | Team | Software | Keywords | link |
| causalai | Salesforce | Python | Discovery | Time Series | basic-inference | https://github.com/salesforce/causalai |
| pycid | Causal Incentives Working Group a collaboration with Oxford, DeepMind and Toronto researchers | Python | bayesian-networks | influence-diagrams | https://github.com/causalincentives/pycid |
| causal-learn | Center for Causal Discovery, pittsbugh | Python | discovery |hidden-causal | Time Series basic-inference | tetrad | https://github.com/causalincentives/pycid |
| CausalImpact | Google, Inc | R | Bayesian time-series | https://github.com/google/CausalImpact |
| EconML | Microsoft Research | Python | Machine Learning Based Estimation of Heterogeneous Treatment Effects | https://econml.azurewebsites.net/index.html |
| causalnex | Petuum | Python | augment model | structure learning | https://causalnex.readthedocs.io/en/latest/index.html |
| dowhy | Microsoft Research | Python | Double ML | Matching | Balancing Synthetic control Outcome-based Learners (e.f. X-Learner) Dif&Dif | basic-inference | https://github.com/py-why/dowhy |
| causalToolbox | University of California | R | X-learner | https://github.com/forestry-labs/causalToolbox |
| showwhy | Microsoft Research | TypeScript | no-code interfaces | https://github.com/microsoft/showwhy |
| causalimages-software | The University of Texas at Austin | R | computer-vision | earth-observation biomedical-image-analysis | https://github.com/cjerzak/causalimages-software |
| gCastle | Huawei Noah's Ark Lab | Python | Structure Learning gradient-based learning | https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle |
| causallib | BiomedSciAI | Python | basic-inference | https://github.com/BiomedSciAI/causallib |
| causal-cmd | Center for Causal Discovery, pittsbugh | java | CMD based | discovery | https://bd2kccd.github.io/docs/causal-cmd/ |
| ResearchMap | UCLA | JS | Optimal experiments | augmented models | https://github.com/ResearchMaps/ |
