<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Noncompliance on Adventures in Why</title><link>https://www.adventuresinwhy.com/tag/noncompliance/</link><description>Recent content in Noncompliance on Adventures in Why</description><generator>Hugo</generator><language>en-us</language><managingEditor>bob@ConvexAnalytics.com (Bob Wilson)</managingEditor><webMaster>bob@ConvexAnalytics.com (Bob Wilson)</webMaster><copyright>&amp;copy; Bob Wilson {year}</copyright><lastBuildDate>Sun, 05 Jan 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://www.adventuresinwhy.com/tag/noncompliance/index.xml" rel="self" type="application/rss+xml"/><item><title>Principal Stratification and Mediation</title><link>https://www.adventuresinwhy.com/post/principal_strata/</link><pubDate>Sun, 05 Jan 2025 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/principal_strata/</guid><description>This post explores principal stratification and mediation analysis as tools for understanding causal effects, decomposing them into direct and indirect components. It covers scenarios like non-compliance, missing outcomes, and surrogate indices, highlighting the importance of assumptions such as no direct effects and no Defiers. Practical methods, including multiple imputation, regression, and matching, are discussed for estimating effects even when key quantities are unobserved. Real-world examples, like marketing lift studies and product funnels, illustrate the relevance of these techniques for addressing complex causal questions.</description></item><item><title>Tests with One-Sided Noncompliance</title><link>https://www.adventuresinwhy.com/post/instrumental_variables/</link><pubDate>Sun, 26 Feb 2023 00:00:00 +0000</pubDate><author>bob@ConvexAnalytics.com (Bob Wilson)</author><guid>https://www.adventuresinwhy.com/post/instrumental_variables/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Tech companies spoil data scientists. It&amp;rsquo;s so easy for us to A/B test
everything. We can alter many aspects of the product from a
configuration UI. We have the sample size to get a good read in as
little as a few days. We have the data infrastructure to analyze and
report results quickly.&lt;/p&gt;</description></item></channel></rss>