Endogenous Treatment Effects Estimators :Stata操作視頻

2021-01-14 江南殘雪




Measuring the Return to Online Advertising: Estimation and Inference of Endogenous Treatment EffectsAuthor

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Shakeeb Khan

Denis Nekipelov

Abstract

In this paper we aim to conduct inference on the 「lift」 effect generated by an online advertisement display: specifically we want to analyze if the presence of the brand ad among the advertisements on the page increases the overall number of consumer clicks on that page. A distinctive feature of online advertising is that the ad displays are highly targeted- the advertising platform evaluates the (unconditional) probability of each consumer clicking on a given ad which leads to a higher probability of displaying the ads that have a higher a priori estimated probability of click. As a result, inferring the causal effect of the ad display on the page clicks by a given consumer from typical observational data is difficult. To address this we use the large scale of our dataset and propose a multi-step estimator that focuses on the tails of the consumer distribution to estimate the true causal effect of an ad display. This 「identification at infinity」 (Chamberlain (1986)) approach alleviates the need for independent experimental randomization but results in nonstandard asymptotics. To validate our estimates, we use a set of large scale randomized controlled experiments that Microsoft has run on its advertising platform. Our dataset has a large number of observations and a large number of variables and we employ LASSO to perform variable selection. Our non-experimental estimates turn out to be quite close to the results of the randomized controlled trials.

Shakeeb Khan & Denis Nekipelov & Justin Rao, 2018. "Measuring the Return to Online Advertising: Estimation and Inference of Endogenous Treatment Effects," Boston College Working Papers in Economics946, Boston College Department of Economics.

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Stata & Python實證前沿與爬蟲分析2019年暑期工作坊

方法應用為基：講解經濟、統計、管理等科學量化前沿方法的機理與Stata & Python實操

經典論文複製：講解高質量經典論文如何使用前沿量化方法

突出研究設計：突出量化方法和經典論文背後的精巧研究設計

強化互動交流：強化講師與學員之間的工作論文和研究計劃交流

江艇：香港科技大學商學院經濟學博士，中國人民大學經濟學院副教授，人大國家發展與戰略研究院研究員，人大微觀數據與實證方法研究中心副主任，美國哥倫比亞大學商學院訪問學者。主要研究領域為經濟增長與發展、城市經濟學、新政治經濟學，在Economics Letters、Review of Development Economics、《經濟研究》、《管理世界》、《世界經濟》等國內外著名學術刊物上發表多篇論文。曾應邀在多所高校講授「應用微觀計量經濟學」短期前沿課程，學員反響熱烈。

司繼春（慧航）：上海對外經貿大學統計與信息學院助理教授，主要研究領域為微觀計量經濟學、產業組織理論。在 Journal of Business and Economic Statistics、《財經研究》等學術刊物上發表多篇論文。其實，大家更熟悉的是知乎上大名鼎鼎的[慧航]，擁有 219,753 個關注者，獲得過 110,578 次贊同，他就是司繼春老師 —— [慧航]。

鄧旭東（大鄧）：哈爾濱工業大學(HIT)管理學院信息管理系統方向在讀博士。曾在多所大學分享數據採集和文本分析培訓課程，運營【公眾號：大鄧和他的Python】主要分享Python、爬蟲、文本分析、機器學習等相關內容。


·  主辦：杭州國商智庫信息技術服務有限公司

·  時間：2019年7月13-18日

·  地點：浙江 · 杭州 · 錢塘新區高教園區東區

·  主講嘉賓：江艇；司繼春（慧航）；鄧旭東（大鄧）

·  授課內容：Stata & Python 實證前沿與爬蟲分析

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