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    <title>Whysen Blog</title>
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    <description>Insights and updates from the world of Causal AI.</description>
    <language>en</language>
      <lastBuildDate>Mon, 01 Dec 2025 00:00:00 GMT</lastBuildDate>

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        <title>Correlation is not Causation</title>
        <link>https://whysen.com/en/blog/articles/2025-12-01-why-correlation-is-not-causation/</link>
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        <pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate>
        <description><![CDATA[Discover why ignoring <strong>causality costs businesses millions</strong> - and how to escape the <strong>correlation trap</strong>.]]></description>
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          <p>We've all heard this phrase, but do we truly understand the cost of ignoring it? Imagine a retailer discovering that customers wearing winter jackets buy less ice cream<sup><a href="#ref-1">[1]</a></sup>. They invest in store heating to reduce jacket wearing - only to find that sales remain flat. The <strong>correlation was real, but the causality was reversed</strong>: cold weather causes both jacket wearing and lower ice cream sales. This type of error costs businesses <strong>millions every year</strong>.</p>
          <p><a href="https://whysen.com/en/blog/articles/2025-12-01-why-correlation-is-not-causation/">Read the full article</a></p>
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      <item>
        <title>The Real Bottleneck in AI</title>
        <link>https://whysen.com/en/blog/articles/2025-12-02-real-bottleneck-in-causal-inference/</link>
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        <pubDate>Tue, 02 Dec 2025 00:00:00 GMT</pubDate>
        <description><![CDATA[Discover why <strong>conceptual clarity</strong> beats more data when making causal claims.]]></description>
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          <p>Discussions on causal inference often revolve around algorithms and data volume. The uncomfortable truth is that <strong>the weakest link is conceptual</strong>: <strong>selection bias</strong>, <strong>missing counterfactuals</strong>, and <strong>misaligned assumptions</strong>. More data does not fix flawed identification.</p>
          <p><a href="https://whysen.com/en/blog/articles/2025-12-02-real-bottleneck-in-causal-inference/">Read the full article</a></p>
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      <item>
        <title>From Models to Decisions</title>
        <link>https://whysen.com/en/blog/articles/2025-12-03-deploying-causal-models-workflow/</link>
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        <pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate>
        <description><![CDATA[A <strong>practical blueprint</strong> for turning causal models into reliable production decisions.]]></description>
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          <p>Researchers produce estimators; engineers ship features. What's missing is a <strong>reproducible and verifiable workflow</strong> that takes causal models from <strong>identification</strong> to <strong>reliable production decisions</strong>. This article proposes a practical blueprint.</p>
          <p><a href="https://whysen.com/en/blog/articles/2025-12-03-deploying-causal-models-workflow/">Read the full article</a></p>
        ]]></content:encoded>
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        <title>Causal Analysis vs. A/B Testing</title>
        <link>https://whysen.com/en/blog/articles/2025-12-04-causal-analysis-beats-ab-tests/</link>
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        <pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate>
        <description><![CDATA[When to choose <strong>causal analysis</strong> over experimentation - a practical guide for product and data leaders.]]></description>
        <content:encoded><![CDATA[
          <p><strong>A/B tests</strong> are powerful, but not always feasible. When experiments are slow, unethical, or impossible, <strong>causal methods</strong> can provide faster, cheaper, and more informative answers - if used correctly.</p>
          <p><a href="https://whysen.com/en/blog/articles/2025-12-04-causal-analysis-beats-ab-tests/">Read the full article</a></p>
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