{"title":"Do Not Confuse Correlation with Causality","authors":"","doi":"10.12987/9780300255881-004","DOIUrl":"https://doi.org/10.12987/9780300255881-004","url":null,"abstract":"","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"42 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79631850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Replicating a Popular Design: The Close Election","authors":"","doi":"10.12987/9780300255881-021","DOIUrl":"https://doi.org/10.12987/9780300255881-021","url":null,"abstract":"","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"171 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85564234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Approximate Matching","authors":"Ben Langmead","doi":"10.12987/9780300255881-017","DOIUrl":"https://doi.org/10.12987/9780300255881-017","url":null,"abstract":"You are free to use these slides. If you do, please sign the guestbook (www.langmead-lab.org/teaching-materials), or email me (ben.langmead@gmail.com) and tell me briey how you're using them. For original Keynote les, email me.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"11 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81736588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Radical empiricism and machine learning research","authors":"J. Pearl","doi":"10.1515/jci-2021-0006","DOIUrl":"https://doi.org/10.1515/jci-2021-0006","url":null,"abstract":"Abstract I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality” stands for the processes that generate the data. I argue for restoring balance to data science through a task-dependent symbiosis of fitting and interpreting, guided by the Logic of Causation.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"47 1","pages":"78 - 82"},"PeriodicalIF":1.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76849388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Peña, Sourabh Vivek Balgi, A. Sjölander, E. Gabriel
{"title":"On the bias of adjusting for a non-differentially mismeasured discrete confounder","authors":"J. Peña, Sourabh Vivek Balgi, A. Sjölander, E. Gabriel","doi":"10.1515/jci-2021-0033","DOIUrl":"https://doi.org/10.1515/jci-2021-0033","url":null,"abstract":"Abstract Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that adjustment for the mismeasured observed variables provides a less biased estimate of the true average causal effect than not adjusting. However, this is not always the case and depends on both the nature of the measurement and confounding. We describe two sets of conditions under which adjusting for a non-deferentially mismeasured proxy comes closer to the unidentifiable true average causal effect than the unadjusted or crude estimate. The first set of conditions apply when the exposure is discrete or continuous and the confounder is ordinal, and the expectation of the outcome is monotonic in the confounder for both treatment levels contrasted. The second set of conditions apply when the exposure and the confounder are categorical (nominal). In all settings, the mismeasurement must be non-differential, as differential mismeasurement, particularly an unknown pattern, can cause unpredictable results.","PeriodicalId":48576,"journal":{"name":"Journal of Causal Inference","volume":"265 1","pages":"229 - 249"},"PeriodicalIF":1.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72830204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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