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    bflc:aap "Villa Robert, Aleix Ruiz de. Causal inference for data science" ;
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        <http://id.loc.gov/authorities/subjects/sh2007000909>,
        <http://id.loc.gov/authorities/subjects/sh85021459>,
        <http://id.loc.gov/authorities/subjects/sh85035862>,
        <http://id.loc.gov/authorities/subjects/sh85066082>,
        <http://id.loc.gov/authorities/subjects/sh85066780>,
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    bf:summary [ a bf:Summary ;
            rdfs:label "Why did you get a particular result? What would have lead to a different outcome? These are the essential questions of causal inference. This powerful methodology improves your decisions by connecting cause and effect-- even when you can't run experiments, A/B tests, or expensive controlled trials. Causal inference for data science introduces techniques to apply causal reasoning to ordinary business scenarios. And with this clearly-written, practical guide, you won't need advanced statistics or high-level math to put causal inference into practice! By applying a simple approach based on Directed Acyclic Graphs (DAGs), you'll learn to assess advertising performance, pick productive health treatments, deliver effective product pricing, and more." ] ;
    bf:supplementaryContent <http://id.loc.gov/vocabulary/msupplcont/index> ;
    bf:tableOfContents [ a bf:TableOfContents ;
            rdfs:label "Part 1. Interence and the role of confounders. Introducing causality -- First steps: Working with confounders -- Applying causal inference -- How machine learning and causal inference can help each other -- Part 2. The adjustment formula in practice. Finding comparable cases with propensity scores -- Direct and indirect effects with linear models -- Dealing with complex graphs -- Advanced tools with the DoubleML library -- Part 3. Other strategies beyond the adjustment formula. Instrumental variables -- Potential outcomes framework -- The effect of a time-related event." ] ;
    bf:title [ a bf:Title ;
            bf:mainTitle "Causal inference for data science" ] .