{"version":"1.0","provider_name":"Microsoft Research","provider_url":"https:\/\/www.microsoft.com\/en-us\/research","author_name":"Vasilis Syrgkanis","author_url":"https:\/\/www.microsoft.com\/en-us\/research\/people\/vasy\/","title":"Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation - Microsoft Research","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"80ftiyCA20\"><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/orthogonal-random-forest-for-heterogeneous-treatment-effect-estimation\/\">Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/orthogonal-random-forest-for-heterogeneous-treatment-effect-estimation\/embed\/#?secret=80ftiyCA20\" width=\"600\" height=\"338\" title=\"&#8220;Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation&#8221; &#8212; Microsoft Research\" data-secret=\"80ftiyCA20\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/* ]]> *\/\n<\/script>\n","description":"We study the problem of estimating heterogeneous treatment effects from observational data, where the treatment policy on the collected data was determined by potentially many confounding observable variables. We propose orthogonal random forest1, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests [Athey et [&hellip;]"}