[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-201797-105":59,"doc-detail-201797-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","qte-quantile-treatment-effects-version-200-package-overview","qte - Quantile Treatment Effects Version 2.0.0 - package简介","","R package qte provides methods to compute Quantile Treatment Effects (QTE) and Quantile Treatment Effect on the Treated (QTT). It covers randomized assignment, selection on observables via Firpo-style approaches, and Difference-in-Differences identification. Version 2.0 unifies staggered treatment adoption through a common API built on ptetools and introduces a lagged-outcome unconfoundedness estimator (lou_qtt). The package supports multiple estimators and plotting utilities.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/qte-quantile-treatment-effects-version-200-package-overview/201797/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/qte-quantile-treatment-effects-version-200-package-overview/201797.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the qte package compute?","Question",{"text":113,"@type":114},"It computes Quantile Treatment Effects (QTE) and Quantile Treatment Effect on the Treated (QTT), with related treated-effect estimands such as ATT in specific identification setups.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which identification cases are covered by qte?",{"text":118,"@type":114},"qte covers randomized treatment assignment, selection on observables after conditioning on covariates, and Difference-in-Differences–based identification for multiple varieties.",{"name":120,"@type":111,"acceptedAnswer":121},"What is new in Version 2.0?",{"text":122,"@type":114},"Version 2.0 adds a unified staggered treatment adoption API for DiD-based estimators and introduces the new lagged-outcome unconfoundedness estimator lou_qtt.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},201797,1788538805,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Package ‘qte’  \nJuly 23, 2026  \nTitle Quantile Treatment Effects Version 2.0.0  \nDescription Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT) . The main cases covered are (i) treatment is randomly assigned,(ii) treatment is as good as randomly assigned after conditioning on covariates (selection on observables) using the methods of Firpo (2007)  \n\u003Cdoi:10.1111/j.1468-0262.2007.00738.x>, and (iii) identification is based on a Difference in Differences assumption, with support for several varieties including Athey and Imbens (2006)  \n\u003Cdoi:10.1111/j.1468-0262.2006.00668.x>, Callaway and Li (2019)  \n\u003Cdoi:10.3982/QE935>, and Callaway, Li, and Oka (2018)  \n\u003Cdoi:10.1016/j.jeconom.2018.06.008> . Version 2.0 adds a unified staggered treatment adoption API (built on 'ptetools') for all DiD-based estimators, as well as a new lagged-outcome unconfoundedness estimator ('lou_qtt') .  \nDepends R (>= 3.5)  \nImports parallel, quantreg, BMisc, [formula.tools](formula.tools), ggplot2, rlang, pbapply, data.table, ptetools (>= 1.0.1)  \nURL [https://bcallaway1](https://bcallaway1) [1.github.io/qte/](1.github.io/qte/),  \n[https://github.com/bcallaway11/qte](https://github.com/bcallaway11/qte)  \nBugReports [https://github.com/bcallaway11/qte/issues](https://github.com/bcallaway11/qte/issues)[ ](https://github.com/bcallaway11/qte/issues)License GPL-3  \nEncoding UTF-8 LazyData true VignetteBuilder knitr, quarto  \nSuggests rmarkdown, knitr, msm, testthat (>= 3.0.0), did, quarto Config/roxygen2/version 8.0.0  \nNeedsCompilation no  \nAuthor Brantly Callaway [aut, cre]  \nMaintainer Brantly Callaway \u003C[brantly.callaway@uga.edu](brantly.callaway@uga.edu)> Repository CRAN  \nDate/Publication 2026-07-23 10:50:15 UTC  \nContents  \nqte-package ......................................... 2  \nautoplot.QTE ........................................ 3  \ncic .............................................. 3  \ncic_gt ............................................ 5  \nddid ............................................. 6  \nddid_gt ........................................... 8  \nlalonde ............................................ 9  \nlalonde.exp ......................................... 10  \nlalonde.exp.panel ...................................... 10  \nlalonde.psid ......................................... 10  \nlalonde.psid.panel ...................................... 11  \nlou_qtt ............................................ 11  \nmdid ............................................. 14  \nmdid_gt ........................................... 16  \npanel_qtt .......................................... 17  \npanel_qtt_gt ......................................... 19  \nplot.QTE .......................................... 20  \nprint.summary.QTE ..................................... 21  \nqdid ............................................. 21  \nqdid_gt ........................................... 23  \nsummary.QTE ........................................ 24  \nunc_qte ........................................... 24  \nIndex 28  \nqte-package qte: A package for computing quantile treatment effects  \nDescription  \nProvides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT) . The main cases covered are (i) treatment is randomly assigned,(ii) treatment is as good as randomly assigned after conditioning on covariates (selection on observables) using the methods of Firpo (2007) doi:10.1111/j.14680262.2007.00738.x, and (iii) identification is based on a Difference in Differences assumption, with support for several varieties including Athey and Imbens (2006) doi:10.1111/j.14680262.2006.00668.x, Callaway and Li (2019) doi:10.3982/QE935, and Callaway, Li, and Oka (2018) doi:10.1016/j.jeconom.2018.06.008 . Version 2.0 adds a unified staggered treatment adoption API (built on ’ptetools’) for all DiD-based estimators, as well as a new lagged-outcome unconfoundedness estimator ","cbCaii2rXoXXgXpt","https://ap.wps.com/l/cbCaii2rXoXXgXpt","pdf",213709,28,"English","# qte-package\n## autoplot.QTE\n## cic\n## cic_gt\n## ddid\n## ddid_gt\n## lalonde\n## lalonde.exp\n## lalonde.exp.panel\n## lalonde.psid\n## lalonde.psid.panel\n## lou_qtt\n## mdid\n## mdid_gt\n## panel_qtt\n## panel_qtt_gt\n## plot.QTE\n## print.summary.QTE\n## qdid\n## qdid_gt\n## summary.QTE\n## unc_qte\n## Index","[{\"question\":\"What does the qte package compute?\",\"answer\":\"It computes Quantile Treatment Effects (QTE) and Quantile Treatment Effect on the Treated (QTT), with related treated-effect estimands such as ATT in specific identification setups.\"},{\"question\":\"Which identification cases are covered by qte?\",\"answer\":\"qte covers randomized treatment assignment, selection on observables after conditioning on covariates, and Difference-in-Differences–based identification for multiple varieties.\"},{\"question\":\"What is new in Version 2.0?\",\"answer\":\"Version 2.0 adds a unified staggered treatment adoption API for DiD-based estimators and introduces the new lagged-outcome unconfoundedness estimator lou_qtt.\"}]","qte - Quantile Treatment Effects Version 2.0.0 - package简介 | PDF",71]