<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Causal Inference - Collection - 滑翔闪'S BLOG</title><link>https://blog.huaxiangshan.com/en/collections/learning-causal-inference/</link><description>滑翔闪'S BLOG</description><generator>Hugo 0.169.0 &amp; FixIt v1.0.0-rc</generator><language>en</language><managingEditor>1450169683@qq.com (滑翔闪)</managingEditor><webMaster>1450169683@qq.com (滑翔闪)</webMaster><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Tue, 01 Apr 2025 15:30:19 +0800</lastBuildDate><atom:link href="https://blog.huaxiangshan.com/en/collections/learning-causal-inference/index.xml" rel="self" type="application/rss+xml"/><item><title> Judge IV (Examiner Designs): Causal Inference via Random Assignment</title><link>https://blog.huaxiangshan.com/en/posts/judge_iv/</link><pubDate>Tue, 01 Apr 2025 15:30:19 +0800</pubDate><author>1450169683@qq.com (滑翔闪)</author><guid>https://blog.huaxiangshan.com/en/posts/judge_iv/</guid><category domain="https://blog.huaxiangshan.com/en/categories/economic-studies/">Economic Studies</category><description>An introduction to an emerging instrumental variable, judge IV. Its strength is that it can further test random assignment, monotonicity, and the exclusion restriction of the instrument.</description></item><item><title>Econometrics: Is the log(y+1) transformation reliable?</title><link>https://blog.huaxiangshan.com/en/posts/log0/</link><pubDate>Sun, 12 May 2024 17:59:49 +0800</pubDate><author>1450169683@qq.com (滑翔闪)</author><guid>https://blog.huaxiangshan.com/en/posts/log0/</guid><category domain="https://blog.huaxiangshan.com/en/categories/economic-studies/">Economic Studies</category><description>Reading notes on &amp;lsquo;Logs with Zeros? Some Problems and Solutions&amp;rsquo;. When the variable being transformed includes zeros, the log(y+1) transformation has some limits in treatment-effect estimation.</description></item><item><title>Causal Effec</title><link>https://blog.huaxiangshan.com/en/posts/effect/</link><pubDate>Wed, 28 Feb 2024 18:27:40 +0800</pubDate><author>1450169683@qq.com (滑翔闪)</author><guid>https://blog.huaxiangshan.com/en/posts/effect/</guid><category domain="https://blog.huaxiangshan.com/en/categories/economic-studies/">Economic Studies</category><description>This paper is a learning note on the recognition effect of causal inference methods, introducing the relationship between ATE, ATT, RCT, ITT, LATE</description></item></channel></rss>