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  1. Three legs are needed for deductive causal inference:
    "Without assumptions regarding construct validity, one cannot accurately label the cause or outcome. Without assumptions regarding external validity, one cannot label the conditions enabling the cause to have an effect. If any of the assumptions regarding internal, construct, and external validity are missing, the claim is not deductively supported. The critical role of theoretical and substantive knowledge in deductive causal inference is illuminated by making such assumptions explicit. This article critically reviews approaches to identification in causal inference while developing a framework called causal specification. Causal specification augments existing identification strategies to enable and justify deductive, generalized claims about causes and effects. In the process, we review a variety of developments in the philosophy of science and causality and interdisciplinary social science methodology."

    Esterling, K., Brady, D. & Schwitzgebel, E. (2025). "The necessity of construct and external validity for deductive causal inference" doi.org/10.1515/jci-2024-0002

    #logics #validity #deduction #generalization #identification #causality #correlations #ProofTheory #PhilSci #truth #causalInference #socialScience

  2. Three legs are needed for deductive causal inference:
    "Without assumptions regarding construct validity, one cannot accurately label the cause or outcome. Without assumptions regarding external validity, one cannot label the conditions enabling the cause to have an effect. If any of the assumptions regarding internal, construct, and external validity are missing, the claim is not deductively supported. The critical role of theoretical and substantive knowledge in deductive causal inference is illuminated by making such assumptions explicit. This article critically reviews approaches to identification in causal inference while developing a framework called causal specification. Causal specification augments existing identification strategies to enable and justify deductive, generalized claims about causes and effects. In the process, we review a variety of developments in the philosophy of science and causality and interdisciplinary social science methodology."

    Esterling, K., Brady, D. & Schwitzgebel, E. (2025). "The necessity of construct and external validity for deductive causal inference" doi.org/10.1515/jci-2024-0002

    #logics #validity #deduction #generalization #identification #causality #correlations #ProofTheory #PhilSci #truth #causalInference #socialScience

  3. Three legs are needed for deductive causal inference:
    "Without assumptions regarding construct validity, one cannot accurately label the cause or outcome. Without assumptions regarding external validity, one cannot label the conditions enabling the cause to have an effect. If any of the assumptions regarding internal, construct, and external validity are missing, the claim is not deductively supported. The critical role of theoretical and substantive knowledge in deductive causal inference is illuminated by making such assumptions explicit. This article critically reviews approaches to identification in causal inference while developing a framework called causal specification. Causal specification augments existing identification strategies to enable and justify deductive, generalized claims about causes and effects. In the process, we review a variety of developments in the philosophy of science and causality and interdisciplinary social science methodology."

    Esterling, K., Brady, D. & Schwitzgebel, E. (2025). "The necessity of construct and external validity for deductive causal inference" doi.org/10.1515/jci-2024-0002

    #logics #validity #deduction #generalization #identification #causality #correlations #ProofTheory #PhilSci #truth #causalInference #socialScience

  4. Three legs are needed for deductive causal inference:
    "Without assumptions regarding construct validity, one cannot accurately label the cause or outcome. Without assumptions regarding external validity, one cannot label the conditions enabling the cause to have an effect. If any of the assumptions regarding internal, construct, and external validity are missing, the claim is not deductively supported. The critical role of theoretical and substantive knowledge in deductive causal inference is illuminated by making such assumptions explicit. This article critically reviews approaches to identification in causal inference while developing a framework called causal specification. Causal specification augments existing identification strategies to enable and justify deductive, generalized claims about causes and effects. In the process, we review a variety of developments in the philosophy of science and causality and interdisciplinary social science methodology."

    Esterling, K., Brady, D. & Schwitzgebel, E. (2025). "The necessity of construct and external validity for deductive causal inference" doi.org/10.1515/jci-2024-0002

    #logics #validity #deduction #generalization #identification #causality #correlations #ProofTheory #PhilSci #truth #causalInference #socialScience

  5. Three legs are needed for deductive causal inference:
    "Without assumptions regarding construct validity, one cannot accurately label the cause or outcome. Without assumptions regarding external validity, one cannot label the conditions enabling the cause to have an effect. If any of the assumptions regarding internal, construct, and external validity are missing, the claim is not deductively supported. The critical role of theoretical and substantive knowledge in deductive causal inference is illuminated by making such assumptions explicit. This article critically reviews approaches to identification in causal inference while developing a framework called causal specification. Causal specification augments existing identification strategies to enable and justify deductive, generalized claims about causes and effects. In the process, we review a variety of developments in the philosophy of science and causality and interdisciplinary social science methodology."

    Esterling, K., Brady, D. & Schwitzgebel, E. (2025). "The necessity of construct and external validity for deductive causal inference" doi.org/10.1515/jci-2024-0002

  6. I like to say one thing to say another because that's how #MissKitty sees things typically. It's all the #same. Going #home I am told it looks more and more like that. I'm not going to put any more effort unless necessary and to understanding the dynamics of who #blocks me. I've made #correlations.

  7. I like to say one thing to say another because that's how #MissKitty sees things typically. It's all the #same. Going #home I am told it looks more and more like that. I'm not going to put any more effort unless necessary and to understanding the dynamics of who #blocks me. I've made #correlations.

  8. Qubits break long-held quantum limit by evolving in superposed time paths

    For decades, physicists believed that even the strangest quantum objects had a hard limit on how strongly their…
    #NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Physics #AnthonyLeggett #correlations #Quantum #Quantumcomputers #quantumobjects #quantumsuperposition #Science
    newsbeep.com/us/349557/

  9. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  10. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  11. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

  12. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  13. "It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."

    Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" bookdown.org/max/FES/

    #correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation

  14. Google Search Rankings Correlate HIGHLY with Websites that publish content, use domain names in their URLs, use internal links to connect pages together, and that appear in results on other search engines.

    #seo #searchengineoptimization #analysis #correlations #webmarketing #digitalmarketing #algorithms #machinelearning #ai #science #math #yourtaghere

  15. Google Search Rankings Correlate HIGHLY with Websites that publish content, use domain names in their URLs, use internal links to connect pages together, and that appear in results on other search engines.

    #seo #searchengineoptimization #analysis #correlations #webmarketing #digitalmarketing #algorithms #machinelearning #ai #science #math #yourtaghere

  16. Google Search Rankings Correlate HIGHLY with Websites that publish content, use domain names in their URLs, use internal links to connect pages together, and that appear in results on other search engines.

    #seo #searchengineoptimization #analysis #correlations #webmarketing #digitalmarketing #algorithms #machinelearning #ai #science #math #yourtaghere

  17. Google Search Rankings Correlate HIGHLY with Websites that publish content, use domain names in their URLs, use internal links to connect pages together, and that appear in results on other search engines.

    #seo #searchengineoptimization #analysis #correlations #webmarketing #digitalmarketing #algorithms #machinelearning #ai #science #math #yourtaghere

  18. Google Search Rankings Correlate HIGHLY with Websites that publish content, use domain names in their URLs, use internal links to connect pages together, and that appear in results on other search engines.

    #seo #searchengineoptimization #analysis #correlations #webmarketing #digitalmarketing #algorithms #machinelearning #ai #science #math #yourtaghere

  19. Correlation studies do NOT reveal how Google's algorithms work. Correlation studies do not identify cause-and-effect, they do not point to or imply cause-and-effect, and they do not explain why anything is happening in Google's search results.

    #seo #searchengineoptimization #google #searchengines #analytics #correlations #statistics #webmarketing #digitalmarketing

  20. Correlation studies do NOT reveal how Google's algorithms work. Correlation studies do not identify cause-and-effect, they do not point to or imply cause-and-effect, and they do not explain why anything is happening in Google's search results.

    #seo #searchengineoptimization #google #searchengines #analytics #correlations #statistics #webmarketing #digitalmarketing

  21. Correlation studies do NOT reveal how Google's algorithms work. Correlation studies do not identify cause-and-effect, they do not point to or imply cause-and-effect, and they do not explain why anything is happening in Google's search results.

    #seo #searchengineoptimization #google #searchengines #analytics #correlations #statistics #webmarketing #digitalmarketing

  22. Correlation studies do NOT reveal how Google's algorithms work. Correlation studies do not identify cause-and-effect, they do not point to or imply cause-and-effect, and they do not explain why anything is happening in Google's search results.

    #seo #searchengineoptimization #google #searchengines #analytics #correlations #statistics #webmarketing #digitalmarketing

  23. Correlation studies do NOT reveal how Google's algorithms work. Correlation studies do not identify cause-and-effect, they do not point to or imply cause-and-effect, and they do not explain why anything is happening in Google's search results.

    #seo #searchengineoptimization #google #searchengines #analytics #correlations #statistics #webmarketing #digitalmarketing

  24. "A major function [of deductive #logic is in] assessing exactly what is involved in asserting some set of propositions. […] By omitting some premiss without which the deduction of some conclusion is not valid, it misrepresents the premiss from which this conclusion is obtained, and hence responsibility for the conclusion. To agree to accept partial responsibility as good enough here is like agreeing to say that somebody was responsible for the dinner when he peeled potatoes and the cook did the rest. The first statement cannot be accepted as an elliptical, but allowable, way of making the second statement. And similarly suppression [of some premiss] enables us to obtain as causally responsible a partially sufficient rather than a fully sufficient causal condition."

    Valerie Plumwood in Australasian Journal of Logic, 2023: ojs.victoria.ac.nz/ajl/issue/v v @rrrichardzach

    #Plumwood #causality #correlations #economics #reason #ProofTheory #PhilSci #truth #science #ethics #ecofeminism #freedom

  25. "A major function [of deductive #logic is in] assessing exactly what is involved in asserting some set of propositions. […] By omitting some premiss without which the deduction of some conclusion is not valid, it misrepresents the premiss from which this conclusion is obtained, and hence responsibility for the conclusion. To agree to accept partial responsibility as good enough here is like agreeing to say that somebody was responsible for the dinner when he peeled potatoes and the cook did the rest. The first statement cannot be accepted as an elliptical, but allowable, way of making the second statement. And similarly suppression [of some premiss] enables us to obtain as causally responsible a partially sufficient rather than a fully sufficient causal condition."

    Valerie Plumwood in Australasian Journal of Logic, 2023: ojs.victoria.ac.nz/ajl/issue/v v @rrrichardzach

    #Plumwood #causality #correlations #economics #reason #ProofTheory #PhilSci #truth #science #ethics #ecofeminism #freedom

  26. "A major function [of deductive #logic is in] assessing exactly what is involved in asserting some set of propositions. […] By omitting some premiss without which the deduction of some conclusion is not valid, it misrepresents the premiss from which this conclusion is obtained, and hence responsibility for the conclusion. To agree to accept partial responsibility as good enough here is like agreeing to say that somebody was responsible for the dinner when he peeled potatoes and the cook did the rest. The first statement cannot be accepted as an elliptical, but allowable, way of making the second statement. And similarly suppression [of some premiss] enables us to obtain as causally responsible a partially sufficient rather than a fully sufficient causal condition."

    Valerie Plumwood in Australasian Journal of Logic, 2023: ojs.victoria.ac.nz/ajl/issue/v v @rrrichardzach

    #Plumwood #causality #correlations #economics #reason #ProofTheory #PhilSci #truth #science #ethics #ecofeminism #freedom

  27. "A major function [of deductive #logic is in] assessing exactly what is involved in asserting some set of propositions. […] By omitting some premiss without which the deduction of some conclusion is not valid, it misrepresents the premiss from which this conclusion is obtained, and hence responsibility for the conclusion. To agree to accept partial responsibility as good enough here is like agreeing to say that somebody was responsible for the dinner when he peeled potatoes and the cook did the rest. The first statement cannot be accepted as an elliptical, but allowable, way of making the second statement. And similarly suppression [of some premiss] enables us to obtain as causally responsible a partially sufficient rather than a fully sufficient causal condition."

    Valerie Plumwood in Australasian Journal of Logic, 2023: ojs.victoria.ac.nz/ajl/issue/v v @rrrichardzach

    #Plumwood #causality #correlations #economics #reason #ProofTheory #PhilSci #truth #science #ethics #ecofeminism #freedom

  28. "A major function [of deductive is in] assessing exactly what is involved in asserting some set of propositions. […] By omitting some premiss without which the deduction of some conclusion is not valid, it misrepresents the premiss from which this conclusion is obtained, and hence responsibility for the conclusion. To agree to accept partial responsibility as good enough here is like agreeing to say that somebody was responsible for the dinner when he peeled potatoes and the cook did the rest. The first statement cannot be accepted as an elliptical, but allowable, way of making the second statement. And similarly suppression [of some premiss] enables us to obtain as causally responsible a partially sufficient rather than a fully sufficient causal condition."

    Valerie Plumwood in Australasian Journal of Logic, 2023: ojs.victoria.ac.nz/ajl/issue/v v @rrrichardzach

  29. Improved AI Process Could Better Predict Water Supplies
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    doi.org/10.1609/aaai.v38i21.30 <-- shared paper
    --
    “A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers [link above] predict water availability from areas in the West where snow amounts aren't being physically measured…”
    #GIS #spatial #mapping #water #hydrology #waterresources #spatialanalysis #spatiotemporal #model #modeling #numericalmodeling #computermodel #AI #snowpack #WesternUSA #USWest #watersecurity #prediction #SnowWaterEquivalent #SWE #irrigation #floodcontrol #powergeneration #drought #management #decisions #SnowTelemetry #SNOTEL #machinelearning #attentionmechanisms #correlations #snowpack

  30. Improved AI Process Could Better Predict Water Supplies
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    doi.org/10.1609/aaai.v38i21.30 <-- shared paper
    --
    “A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers [link above] predict water availability from areas in the West where snow amounts aren't being physically measured…”
    #GIS #spatial #mapping #water #hydrology #waterresources #spatialanalysis #spatiotemporal #model #modeling #numericalmodeling #computermodel #AI #snowpack #WesternUSA #USWest #watersecurity #prediction #SnowWaterEquivalent #SWE #irrigation #floodcontrol #powergeneration #drought #management #decisions #SnowTelemetry #SNOTEL #machinelearning #attentionmechanisms #correlations #snowpack

  31. Improved AI Process Could Better Predict Water Supplies
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    doi.org/10.1609/aaai.v38i21.30 <-- shared paper
    --
    “A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers [link above] predict water availability from areas in the West where snow amounts aren't being physically measured…”
    #GIS #spatial #mapping #water #hydrology #waterresources #spatialanalysis #spatiotemporal #model #modeling #numericalmodeling #computermodel #AI #snowpack #WesternUSA #USWest #watersecurity #prediction #SnowWaterEquivalent #SWE #irrigation #floodcontrol #powergeneration #drought #management #decisions #SnowTelemetry #SNOTEL #machinelearning #attentionmechanisms #correlations #snowpack

  32. Improved AI Process Could Better Predict Water Supplies
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    doi.org/10.1609/aaai.v38i21.30 <-- shared paper
    --
    “A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers [link above] predict water availability from areas in the West where snow amounts aren't being physically measured…”
    #GIS #spatial #mapping #water #hydrology #waterresources #spatialanalysis #spatiotemporal #model #modeling #numericalmodeling #computermodel #AI #snowpack #WesternUSA #USWest #watersecurity #prediction #SnowWaterEquivalent #SWE #irrigation #floodcontrol #powergeneration #drought #management #decisions #SnowTelemetry #SNOTEL #machinelearning #attentionmechanisms #correlations #snowpack

  33. Improved AI Process Could Better Predict Water Supplies
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    doi.org/10.1609/aaai.v38i21.30 <-- shared paper
    --
    “A new computer model uses a better artificial intelligence process to measure snow and water availability more accurately across vast distances in the West, information that could someday be used to better predict water availability for farmers and others. The researchers [link above] predict water availability from areas in the West where snow amounts aren't being physically measured…”

  34. I used to be annoyed by the absurdly low p-values that can be obtained when doing #correlations with thousands of data points. Permutation analysis could help to see if the observed relationships might be a random occurence.

    Searching now for tools that automate the process, while also explaining clearly what is being done.

    #statistics #pvalue #research

  35. I used to be annoyed by the absurdly low p-values that can be obtained when doing #correlations with thousands of data points. Permutation analysis could help to see if the observed relationships might be a random occurence.

    Searching now for tools that automate the process, while also explaining clearly what is being done.

    #statistics #pvalue #research

  36. I used to be annoyed by the absurdly low p-values that can be obtained when doing #correlations with thousands of data points. Permutation analysis could help to see if the observed relationships might be a random occurence.

    Searching now for tools that automate the process, while also explaining clearly what is being done.

    #statistics #pvalue #research

  37. Bitcoin (BTC) Slides Back Below $30K, But Should Pump if These Correlations Re-emerge - Bitcoin (BTC), the cryptocurrency that powers the world’s largest decentralized cryptogra... - cryptonews.com/news/bitcoin-bt #correlations #bitcoinnews #bitcoin #btc #dxy

  38. Bitcoin (BTC) Slides Back Below $30K, But Should Pump if These Correlations Re-emerge - Bitcoin (BTC), the cryptocurrency that powers the world’s largest decentralized cryptogra... - cryptonews.com/news/bitcoin-bt #correlations #bitcoinnews #bitcoin #btc #dxy

  39. Bitcoin (BTC) Slides Back Below $30K, But Should Pump if These Correlations Re-emerge - Bitcoin (BTC), the cryptocurrency that powers the world’s largest decentralized cryptogra... - cryptonews.com/news/bitcoin-bt #correlations #bitcoinnews #bitcoin #btc #dxy

  40. Bitcoin (BTC) Slides Back Below $30K, But Should Pump if These Correlations Re-emerge - Bitcoin (BTC), the cryptocurrency that powers the world’s largest decentralized cryptogra... - cryptonews.com/news/bitcoin-bt #correlations #bitcoinnews #bitcoin #btc #dxy

  41. 🔥 In this tutorial, we'll show you to install Giskard #Python #library. In just 4 lines of code, you will discover vulnerabilities, such as:
    #Performance biases.
    #Data leakage.
    ✅ Spurious #correlations.
    #Overconfidence issues.
    #Underconfidence issues.

    [2/4]

  42. 🔥 In this tutorial, we'll show you to install Giskard . In just 4 lines of code, you will discover vulnerabilities, such as:
    biases.
    leakage.
    ✅ Spurious .
    issues.
    issues.

    [2/4]