Posts by Tags

behairal economics

Список для чтения по поведенческой и экспериментальной экономике

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.

category1

Future Blog Post

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

category2

Future Blog Post

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

causal-ml-book

causality

Fresh Example of Simpson’s Paradox in Clinical Trials

A fresh example of Simpson’s Paradox in the wild has recently emerged! Here’s the story: some researchers and policymakers argue that the NIH (National Institutes of Health) funds too many “small and crappy” trials, which has been regularly reported in the press.

Trends from the Third Annual Causal Data Science Meeting

Not so long ago, the third annual Causal Data Science Meeting was held on November 7-8. Among the trends this year, it is worth noting that, unlike in previous years, there are more and more papers on methods directly working in or being developed for production environments.

List of Causal Libraries

NameTeamSoftwareKeywordslink
causalaiSalesforcePythonDiscovery | Time Series | basic-inferencehttps://github.com/salesforce/causalai
pycidCausal Incentives Working
Group a collaboration with
Oxford, DeepMind
and Toronto researchers
Pythonbayesian-networks | influence-diagramshttps://github.com/causalincentives/pycid
causal-learnCenter for Causal Discovery, pittsbughPythondiscovery |hidden-causal | Time Series
basic-inference | tetrad
https://github.com/causalincentives/pycid
CausalImpactGoogle, IncRBayesian time-serieshttps://github.com/google/CausalImpact
EconMLMicrosoft ResearchPythonMachine Learning Based Estimation
of Heterogeneous Treatment Effects
https://econml.azurewebsites.net/index.html
causalnexPetuumPythonaugment model | structure learninghttps://causalnex.readthedocs.io/en/latest/index.html
dowhyMicrosoft ResearchPythonDouble ML | Matching | Balancing
Synthetic control
Outcome-based Learners (e.f. X-Learner)
Dif&Dif | basic-inference
https://github.com/py-why/dowhy
causalToolboxUniversity of CaliforniaRX-learnerhttps://github.com/forestry-labs/causalToolbox
showwhyMicrosoft ResearchTypeScriptno-code interfaceshttps://github.com/microsoft/showwhy
causalimages-software
The University of Texas at Austin
Rcomputer-vision | earth-observation
biomedical-image-analysis
https://github.com/cjerzak/causalimages-software
gCastleHuawei Noah's Ark LabPythonStructure Learning
gradient-based learning
https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
causallibBiomedSciAIPythonbasic-inferencehttps://github.com/BiomedSciAI/causallib
causal-cmdCenter for Causal Discovery, pittsbughjavaCMD based | discoveryhttps://bd2kccd.github.io/docs/causal-cmd/
ResearchMapUCLAJSOptimal experiments | augmented modelshttps://github.com/ResearchMaps/

Список для чтения по поведенческой и экспериментальной экономике

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.

conferences

Trends from the Third Annual Causal Data Science Meeting

Not so long ago, the third annual Causal Data Science Meeting was held on November 7-8. Among the trends this year, it is worth noting that, unlike in previous years, there are more and more papers on methods directly working in or being developed for production environments.

cool posts

Future Blog Post

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

data science

Trends from the Third Annual Causal Data Science Meeting

Not so long ago, the third annual Causal Data Science Meeting was held on November 7-8. Among the trends this year, it is worth noting that, unlike in previous years, there are more and more papers on methods directly working in or being developed for production environments.

effectiveness

eng

Fresh Example of Simpson’s Paradox in Clinical Trials

A fresh example of Simpson’s Paradox in the wild has recently emerged! Here’s the story: some researchers and policymakers argue that the NIH (National Institutes of Health) funds too many “small and crappy” trials, which has been regularly reported in the press.

Trends from the Third Annual Causal Data Science Meeting

Not so long ago, the third annual Causal Data Science Meeting was held on November 7-8. Among the trends this year, it is worth noting that, unlike in previous years, there are more and more papers on methods directly working in or being developed for production environments.

List of Causal Libraries

NameTeamSoftwareKeywordslink
causalaiSalesforcePythonDiscovery | Time Series | basic-inferencehttps://github.com/salesforce/causalai
pycidCausal Incentives Working
Group a collaboration with
Oxford, DeepMind
and Toronto researchers
Pythonbayesian-networks | influence-diagramshttps://github.com/causalincentives/pycid
causal-learnCenter for Causal Discovery, pittsbughPythondiscovery |hidden-causal | Time Series
basic-inference | tetrad
https://github.com/causalincentives/pycid
CausalImpactGoogle, IncRBayesian time-serieshttps://github.com/google/CausalImpact
EconMLMicrosoft ResearchPythonMachine Learning Based Estimation
of Heterogeneous Treatment Effects
https://econml.azurewebsites.net/index.html
causalnexPetuumPythonaugment model | structure learninghttps://causalnex.readthedocs.io/en/latest/index.html
dowhyMicrosoft ResearchPythonDouble ML | Matching | Balancing
Synthetic control
Outcome-based Learners (e.f. X-Learner)
Dif&Dif | basic-inference
https://github.com/py-why/dowhy
causalToolboxUniversity of CaliforniaRX-learnerhttps://github.com/forestry-labs/causalToolbox
showwhyMicrosoft ResearchTypeScriptno-code interfaceshttps://github.com/microsoft/showwhy
causalimages-software
The University of Texas at Austin
Rcomputer-vision | earth-observation
biomedical-image-analysis
https://github.com/cjerzak/causalimages-software
gCastleHuawei Noah's Ark LabPythonStructure Learning
gradient-based learning
https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
causallibBiomedSciAIPythonbasic-inferencehttps://github.com/BiomedSciAI/causallib
causal-cmdCenter for Causal Discovery, pittsbughjavaCMD based | discoveryhttps://bd2kccd.github.io/docs/causal-cmd/
ResearchMapUCLAJSOptimal experiments | augmented modelshttps://github.com/ResearchMaps/

experiments

Список для чтения по поведенческой и экспериментальной экономике

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.

libraries

lists

List of Causal Libraries

NameTeamSoftwareKeywordslink
causalaiSalesforcePythonDiscovery | Time Series | basic-inferencehttps://github.com/salesforce/causalai
pycidCausal Incentives Working
Group a collaboration with
Oxford, DeepMind
and Toronto researchers
Pythonbayesian-networks | influence-diagramshttps://github.com/causalincentives/pycid
causal-learnCenter for Causal Discovery, pittsbughPythondiscovery |hidden-causal | Time Series
basic-inference | tetrad
https://github.com/causalincentives/pycid
CausalImpactGoogle, IncRBayesian time-serieshttps://github.com/google/CausalImpact
EconMLMicrosoft ResearchPythonMachine Learning Based Estimation
of Heterogeneous Treatment Effects
https://econml.azurewebsites.net/index.html
causalnexPetuumPythonaugment model | structure learninghttps://causalnex.readthedocs.io/en/latest/index.html
dowhyMicrosoft ResearchPythonDouble ML | Matching | Balancing
Synthetic control
Outcome-based Learners (e.f. X-Learner)
Dif&Dif | basic-inference
https://github.com/py-why/dowhy
causalToolboxUniversity of CaliforniaRX-learnerhttps://github.com/forestry-labs/causalToolbox
showwhyMicrosoft ResearchTypeScriptno-code interfaceshttps://github.com/microsoft/showwhy
causalimages-software
The University of Texas at Austin
Rcomputer-vision | earth-observation
biomedical-image-analysis
https://github.com/cjerzak/causalimages-software
gCastleHuawei Noah's Ark LabPythonStructure Learning
gradient-based learning
https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
causallibBiomedSciAIPythonbasic-inferencehttps://github.com/BiomedSciAI/causallib
causal-cmdCenter for Causal Discovery, pittsbughjavaCMD based | discoveryhttps://bd2kccd.github.io/docs/causal-cmd/
ResearchMapUCLAJSOptimal experiments | augmented modelshttps://github.com/ResearchMaps/

rus

Список для чтения по поведенческой и экспериментальной экономике

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.

simpson-paradox

Fresh Example of Simpson’s Paradox in Clinical Trials

A fresh example of Simpson’s Paradox in the wild has recently emerged! Here’s the story: some researchers and policymakers argue that the NIH (National Institutes of Health) funds too many “small and crappy” trials, which has been regularly reported in the press.

software

List of Causal Libraries

NameTeamSoftwareKeywordslink
causalaiSalesforcePythonDiscovery | Time Series | basic-inferencehttps://github.com/salesforce/causalai
pycidCausal Incentives Working
Group a collaboration with
Oxford, DeepMind
and Toronto researchers
Pythonbayesian-networks | influence-diagramshttps://github.com/causalincentives/pycid
causal-learnCenter for Causal Discovery, pittsbughPythondiscovery |hidden-causal | Time Series
basic-inference | tetrad
https://github.com/causalincentives/pycid
CausalImpactGoogle, IncRBayesian time-serieshttps://github.com/google/CausalImpact
EconMLMicrosoft ResearchPythonMachine Learning Based Estimation
of Heterogeneous Treatment Effects
https://econml.azurewebsites.net/index.html
causalnexPetuumPythonaugment model | structure learninghttps://causalnex.readthedocs.io/en/latest/index.html
dowhyMicrosoft ResearchPythonDouble ML | Matching | Balancing
Synthetic control
Outcome-based Learners (e.f. X-Learner)
Dif&Dif | basic-inference
https://github.com/py-why/dowhy
causalToolboxUniversity of CaliforniaRX-learnerhttps://github.com/forestry-labs/causalToolbox
showwhyMicrosoft ResearchTypeScriptno-code interfaceshttps://github.com/microsoft/showwhy
causalimages-software
The University of Texas at Austin
Rcomputer-vision | earth-observation
biomedical-image-analysis
https://github.com/cjerzak/causalimages-software
gCastleHuawei Noah's Ark LabPythonStructure Learning
gradient-based learning
https://github.com/huawei-noah/trustworthyAI/tree/master/gcastle
causallibBiomedSciAIPythonbasic-inferencehttps://github.com/BiomedSciAI/causallib
causal-cmdCenter for Causal Discovery, pittsbughjavaCMD based | discoveryhttps://bd2kccd.github.io/docs/causal-cmd/
ResearchMapUCLAJSOptimal experiments | augmented modelshttps://github.com/ResearchMaps/

statistics

Fresh Example of Simpson’s Paradox in Clinical Trials

A fresh example of Simpson’s Paradox in the wild has recently emerged! Here’s the story: some researchers and policymakers argue that the NIH (National Institutes of Health) funds too many “small and crappy” trials, which has been regularly reported in the press.

teamwork

textbooks