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9 - Difference-in-Differences 33:01. We will analyze the emerging competitive landscape and the strategic dynamics in play. Susan Athey Economics of Technology Professor, Senior Fellow at the Stanford Institute for Economic Policy Research and Professor, by courtesy, of Economics Bio ACADEMIC APPOINTMENTS ... • Machine Learning and Causal Inference for Policy Evaluation Athey, S., Assoc Comp Machinery Found insideThis book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the ... Project-based courses pair students with external organizations to solve real-world business problems. This course is part of the GSB's new Action Learning Program, in which you will work on real business challenges under the guidance of faculty. Economics 980m (Harvard), Market Design. Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva. Susan Athey and Guido W. Imbens NBER Working Paper No. Susan is also on the Board of Directors of Lending Club, Expedia, Ripple, Turo, and more. Susan and Auren dive into the role of tech economists, … Prerequisites: Some experience with statistical analysis and the R statistical package. Single crossing properties and the existence of pure strategy equilibria in games of incomplete information. Methods expertise include longitudinal analysis, survival analysis, spatial statistics, pragmatic trials, nonparametric inference, causal inference, and computational statistics. Susan Athey. Generalized Random Forests. Susan Athey and Stefan Wager. A curated list of YouTube channels and videos that I follow/recommend to build intuition around AI/ML concepts. Students will have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Causal Inference cheat sheet for data scientists. Wager and Athey, “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests,” Journal of the American Statistical Association 113, no. Publisher description Reading list. Even if none of the individual proxies satis es the statistical surrogacy criterion by itself, using multiple proxies can be useful in causal inference. Toward causal inference with interference. January 7-9, 2018 . Found inside – Page iThe aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Stanford Graduate School of Business professor and economist recognized for pioneering and innovative scholarship on markets. A Paradox of the Digital Age, Susan Athey: How Big Data Changes Business Management, Stanford GSB Looks Back at One Year of Sheltering in Place, Susan Athey Honored for Innovative Market Research, How Professors Are Teaching in a Coronavirus World, Action Learning Program Brings Experiential Learning to Forefront of Curriculum, Golub Capital Social Impact Lab Aims to Enhance Social Sector Effectiveness, From the Dean: Staying Ahead in the Digital Age, New Faculty Enrich the Stanford GSB Experience, John Roberts Altered the Study of Industrial Organization, Overview of Center for Entrepreneurial Studies, Overview of Certificate & Award Recipients, Overview of How We are Taking a Stand Against Racism, Overview of Real-time Analysis and Investment Lab (RAIL), Overview of Facilitation Training Program, Overview of the Impact Design Immersion Fellowship, Personal Information, Activities & Awards, Overview of Operations, Information & Technology, Driving Innovation and New Ventures in Established Organizations for Teams, Alison Elliott Exceptional Achievement Award, John W. Gardner Volunteer Leadership Award, Jack McDonald Military Service Appreciation Award, Overview of Long-Term Career & Executive Coaches, Overview of Alumni Consulting Team Volunteers, Overview of Stanford GSB Alumni Association, Overview of Companies, Organizations, & Recruiters, Overview of Recruiting Stanford GSB Talent, Overview of Leveraging Stanford GSB Talent, Overview of Internships & Experiential Programs, Overview of Alumni Consulting Team for Nonprofits, Social Innovation & Nonprofit Management Resources, PhD, Economics, Stanford Graduate School of Business, 1995, BA, Economics, Computer Science, and Mathematics, Duke University, 1991, Professor, Stanford University Graduate School of Business , 2013-present, Professor of Economics, Harvard, 2006-2012, Holbrook Working Professor of Economics, Stanford, 2004-2006, Associate Professor of Economics, Stanford, 2001-2003, Castle Krob Career Development Associate Professor of Economics, MIT, 1998-2001, Assistant Professor of Economics, MIT, 1995-1997, R. Michael and Mary Shanahan Faculty Fellow for 2020–21, Von Neumann Prize, Rajk László College for Advanced Studies, 2019, Fellow, International Association of Applied Econometrics, elected 2019, Fellow, Game Theory Society, elected 2017, Codirector, Digital Business: Data, Decisions & Platform Strategy Initiative, Stanford GSB, 2015, Spence Faculty Fellow, Stanford GSB, 2013–14, Fellow, Society for the Advancement of Economic Theory, 2013, Member, National Academy of Science, elected 2012, Fellow, American Academy of Arts and Sciences, elected 2008, Fellow, Econometric Society, elected 2004, Guggenheim Faculty Scholar, Stanford University, 2004–06, Undergraduate Economics Association Teaching Award, 1995–96, Stanford University Lieberman Fellow, 1994–95, State Farm Dissertation Award in Business, 1994, National Science Foundation Graduate Fellowship, 1991–94, Jaedicke Scholar, Stanford Graduate School of Business, 1992–93, Mary Love Collins Scholarship, Chi Omega Foundation, 1991–92, Duke University Alice Baldwin Memorial Scholarship, 1990–91, Member, President’s Committee for the National Medal of Science (Presidential Appointment), 2014-Present, Member, National Academies Board on Science, Technology and Economic Policy Innovation Policy Form, 2013-present, Member, National Academies Committee on Science, Engineering, and Public Policy, 2013-present, Member, President’s Committee for the National Medal of Science (Presidential Appointment), 2011-2013, Member, Nominating Committee for American Academy of Arts and Sciences, 2011-2012. Authors: Stefan Wager, Susan Athey. Why Do Technology Companies Hire Economists? 24963 August 2018 JEL No. Journal of the American Statistical Association 113.523 (2018): 1228-1242. Found insideThis text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis." —Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the ... Two brilliant economists, from very different backgrounds, who decided that they’d work together, pool their comparative advantages in machine learning and causal inference, and create what sometimes feels like an endless number of toys for the rest of us. We analyze this evolution from the perspective of three main constituents: 1) Marketers who rely on advertising to launch and sustain product sales, 2) Publishers and media owners for whom advertising often represents the largest source of monetization, and 3) Advertising agencies who design, plan and buy media for advertising campaigns. When Should You Adjust Standard Errors for Clustering? ... Students will have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. MGTECON 634, Machine Learning and Causal Inference. Susan Athey explains how Bitcoin works, and why virtual digital currency might change the way consumers and financial institutions do business. In this book, prominent development economists discuss the use and impact of one of the most significant of these new methods, randomized control trials (RCTs) and field experiments. Machine Learning and Causal Inference Susan Athey – Stanford University Athey and Imbens (Recursive Partitioning for Heterogeneous Treatment E↵ects, PNAS, 2016) Wager and Athey (Estimation and Inference of Causal E↵ects with Random Forests, JASA, 2018) Athey, Tibshirani and Wager (Generalized Random Forests, 2016) “Sampling-Based Versus Design-Based Uncertainty in Regression Analysis.” ... Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction. Susan Athey’s research is in the areas of industrial organization, microeconomic theory, and applied econometrics. Authors: Stefan Wager, Susan Athey. Evolve as a leader in an executive education program that reinvigorates and ramps up your professional journey. ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. In Preparation: Koenecke, Allison, Susan Athey, and Sharad Goel. WRITTEN TUTORIALS 1. [paper, arxiv] Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, and Ruoqing Zhu. Causal inference and uplift modeling a review of the literature. 3. To maximize the potential of big data, firms are looking for employees with a new skill set. She is the founding director of the Golub Capital Social Impact Lab at Stanford GSB, and associate director of the Stanford Institute for Human-Centered Artificial Intelligence. She has also studied dynamic mechanisms and games with incomplete information, comparative statics under uncertainty, and econometric methods for analyzing auction models. Found inside – Page iIn addition to econometric essentials, this book covers important new extensions as well as how to get standard errors right. \The E ect of Firearm Laws on Gun-In icted Suicides." I’ve been a longtime admirer of Susan Athey and Guido Imbens since as far back as I can remember. ... to be used for provably valid statistical inference. Read Online or Download "Semiparametric And Robust Methods For Complex Parameters In Causal Inference" ebook in PDF, Epub, Tuebl and Mobi. Causal Terminology. At a Future of Work forum, experts say demographic shifts, not artificial intelligence, create the biggest challenges for today’s workplace. Susan Athey Stanford Graduate School of Business Machine Learning and Causal Inference for Heterogeneous Treatment Effects [link is to a series of papers] This talk will review recently developed methods to apply machine learning methods to causal inference problems, including the problems of estimating heterogeneous treatment effects, for example, in A/B testing, as well as This essential guide to doing social research in this fast-evolving digital age explains how the digital revolution is transforming the way social scientists observe behavior, ask questions, run experiments, and engage in mass ... STRAMGT 517, Topics in Digital Business. As one of the first “tech economists,” she served as consulting chief economist for Microsoft Corporation for six years, and now serves on the boards of Expedia, Lending Club, Rover, Turo, and Ripple, as well as nonprofit Innovations for Poverty Action. Recursive Partitioning for Heterogeneous Causal Effects: Guido W. Imbens: PNAS, … \The E ect of Firearm Laws on Gun-In icted Suicides." Guido Imbens is The Applied Econometrics Professor and Professor of Economics at the Stanford Graduate School of Business. Telling cats from dogs is easy. MGTECON 512, The Economics of Internet Search. Google Scholar Cross Ref; Susan Athey and Guido W. Imbens. Sørensen, Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager, Amrita Ahuja, Susan Athey, Arthur Baker, Eric Budish, Juan Camilo Castillo, Rachel Glennerster, Scott Duke Kominers, Michael Kremer, Jean Lee, Canice Prendergast, Christopher M. Snyder, Alex Tabarrok, Brandon Joel Tan, Witold Więcek, Ittai Abraham, Susan Athey, Moshe Babaiof, Michael D. Grubb, Susan Athey, Rena Conti, Richard Frank, Jonathan Gruber, Erik Sverdrup, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, Erik Sverdrup, Ayush Kanodia, Zhengyuan Zhou, Susan Athey, Stefan Wager, Susan Athey, Kevin A. Bryan, Joshua S. Gans, Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li, Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido W. Imbens, Susan Athey, Mohsen Bayati, Guido W. Imbens, Zhaonan Qu, Susan Athey, Julie Tibshirani, Stefan Wager, Alberto Abadie, Susan Athey, Guido W. Imbens, Jeffrey M. Wooldridge, Susan Athey, Francisco J. R. Ruiz, David M. Blei, Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt, Susan Athey, Emilio Calvano, Joshua S. Gans, Susan Athey, Guido W. Imbens, Stefan Wager, Zhengyuan Zhou, Panayotis Mertikopoulos, Susan Athey, Nicholas Bambos, Peter Glynn, Yinyu Ye, Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li, Susan Athey, Dean Eckles, Guido W. Imbens, Susan Athey, Guido W. Imbens, Thai Pham, Stefan Wager, Maja Rudolph, Francisco Ruiz, Susan Athey, Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei, Susan Athey, Dominic Coey, Jonathan Levin, Susan Athey, Jonathan Levin, Enrique Seira, Susan Athey, Larry Katz, Alan Krueger, Steve Levitt, James Poterba, Susan Athey, Andrew Atkeson, Patrick J. Kehoe, Susan Athey, Kyle Bagwell, Chris Sanchirico, Ruohan Zhan, Vitor Hadad, David A. Hirshberg, Susan Athey, Ruohan Zhan, Zhimei Ren, Susan Athey, Zhengyuan Zhou, Jiaming Zeng, Michael F. Gensheimer, Daniel L. Rubin, Susan Athey, Ross D. Shachter, Amrita Ahuja, Susan Athey, Arthur Baker, Eric Budish, Juan Camilo Castillo, Rachel Glennerster, Scott Duke Kominers, Michael Kremer, Jean Lee, Canice Prendergast, Christopher M. Snyder, Alex Tabarrok, Brandon Joel Tan, Witold Wiecek, Molly Offer-Westort, Leah R. Rosenzweig, Susan Athey, Michael Powell, Allison Koenecke, James Brian Byrd, Akihiko Nishimura, Maximilian F. Konig, Ruoxuan Xiong, Sadiqa Mahmood, Vera Mucaj, Chetan Bettegowda, Liam Rose, Suzanne Tamang, Adam Sacarny, Brian Caffo, Susan Athey, Elizabeth A. Stuart, Joshua T. Vogelstein, Ruoxuan Xiong, Susan Athey, Guido W. Imbens, Mohsen Bayati, Susan Athey, Juan Camilo Castillo, Bharat Chandar, Susan Athey, Raj Chetty, Guido W. Imbens, Hyunseung Kang, Jonathan Johannemann, Vitor Hadad, Susan Athey, Stefan Wager, Rob Donnelly, Francisco R, Ruiz, David Blei, Susan Athey, Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager, Susan Athey, Billy Ferguson, Mathew Gentzkow, Tobias Schmidt, Maria Dimakopoulou, Susan Athey, Guido W. Imbens, Susan Athey, Zhengyuan Zhou, Stefan Wager, Alberto Abadie, Susan Athey, Guido W. Imbens, Jeffrey Wooldridge, Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei, Susan Athey, Christian Catalini, Catherine E. Tucker, Susan Athey, Julie Tibshirani, Stefan Wager, Susan Athey, Mohsen Bayati, Nick Doudchenko, Guido W. Imbens, Khashayar Khosravi, Susan Athey, Emilio Calvano, Saumitra Jha, Susan Athey, Iva Parashkevov, Vishnu Sarukkai, Jing Xia, Susan Athey, Christopher Avery, Peter Zemsky, Susan Athey, Joshua Gans, Scott Schaefer, Scott Stern, Stanford Institute of Economic Policy Research, Big-Data Initiative in Intl. Professor of Economics (by courtesy), School of Humanities and Sciences, Senior Fellow, Stanford Institute for Economic Policy Research, Director, Golub Capital Social Impact Lab, Associate Director, Stanford Institute for Human-Centered Artificial Intelligence. The distinction ML as prediction/regularisation tool vs causal inference is not really as clear-cut. How organizations--including Google, StubHub, Airbnb, and Facebook--learn from experiments in a data-driven world. This book explores the evolving role of experiments in corporate and government decision making. 3. We will cover key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. 8. Cambridge University Press. For alternate discussion on causality, read up on Rubin’s Potential Outcomes Framework. R Python: Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests: S. Powers et al., The purpose of this workshop is to bring together experts from different fields to discuss the relationships between machine learning and causal inference and to discuss and highlight the formalization and algorithmization of causality toward achieving human-level machine intelligence. Academics, investors, and nonprofit leaders on how to find innovative solutions for the world’s starkest problems. Susan Athey wants to help machine-learning applications look beyond correlation and into root causes. Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. to inferences implement, However, about for financial, in the many effect cases, political, of experiments a policy or ethical is a ... causal inference from observational data. View athey.pdf from MTH 237 at Alabama A&M University. Susan Athey discusses “Designing Online Advertising Markets at the Algorithmic Game Theory and Practice” at a talk held at the Simons Institute for the Theory Computing, UC Berkeley. "Estimation and inference of heterogeneous treatment effects using random forests." 2020. Estimating Treatment Effects with Causal Forests: An Application. This is a team-based course where students will work on a project to improve a product using data and experimentation. Graduate Economics Education and Student Outcomes, Identification and Inference in Nonlinear Difference-In-Difference Models, The Optimal Degree of Discretion in Monetary Policy, The Impact of Information Technology on Emergency Health Care Outcomes, Monotone Comparative Statics Under Uncertainty, Identification of Standard Auction Models, Optimal Collusion with Private Information, Single Crossing Properties and the Existence of Pure Strategy Equilibria in Games of Incomplete Information, Organizational Design: Decision Rights and Incentive Contracts, Information and Competition in U.S. Forest Service Timber Auctions, Information Technology and Training in Emergency Call Centers, Product and Process Flexibility in an Innovative Environment, The Impact of Machine Learning on Economics, The Nature and Incidence of Software Piracy: Evidence from Windows, Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles, Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits, Policy Learning with Adaptively Collected Data, Uncovering Interpretable Potential Confounders in Electronic Medical Records, Optimal Policies to Battle the Coronavirus “Infodemic” Among Social Media Users in Sub-Saharan Africa: Pre-analysis Plan, A How-To Guide for Conducting Retrospective Analyses: Example COVID-19 Study, Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes, Optimal Experimental Design for Staggered Rollouts, Service Quality in the Gig Economy: Empirical Evidence about Driving Quality at Uber, The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely, Sufficient Representations for Categorical Variables, Counterfactual Inference for Consumer Choice Across Many Product Categories, Estimating Treatment Effects with Causal Forests: An Application, Estimation Considerations in Contextual Bandits, Offline Multi-Action Policy Learning: Generalization and Optimization. “ the internet focus on research methods and results from theory, empirical work, econometrics and machine learning causal. 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